Publications - Salk Institute for Biological Studies

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Bartol, T.M., Ordyan, M., Sejnowski, T.J., Rangamani, P., Kennedy, M.B. A spatial model of autophosphorylation of CaMKII in a glutamatergic spine suggests a network-driven kinetic mechanism for bistable changes in synaptic strength. (2024) bioRxiv. DOI: 10.1101/2024.02.02.578696

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Sameni, S., Bartol, T.M., Corey-Bloom, J., Sejnowski, T.J. Computer simulations predict the impact of neuronal atrophy on the calcium dynamics in Huntington's disease. (2024) PNAS Nexus. 3(1):pgad443. DOI: 10.1093/pnasnexus/pgad443

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Samavat, M., Bartol, T.M., Bromer, C., Hubbard, D.D., Hanka, D.C., Kuwajima, M., Mendenhall, J.M., Parker, P.H., Bowden, J.B., Abraham, W.C., Sejnowski, T.J., Harris, K.M. Long-Term Potentiation Produces a Sustained Expansion of Synaptic Information Storage Capacity in Adult Rat Hippocampus. (2024) bioRxiv. DOI: 10.1101/2024.01.12.574766

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Lainscsek, C., Salami, P., Carvalho, V.R., Mendes, E.M.A.M., Fan, M., Cash, S.S., Sejnowski, T.J. Network-motif delay differential analysis of brain activity during seizures. (2023) Chaos. 33(12). DOI: 10.1063/5.0165904

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Tsuda, B., Richmond, B.J., Sejnowski, T.J. Exploring strategy differences between humans and monkeys with recurrent neural networks. (2023) PLOS Computational Biology. 19(11):e1011618. DOI: 10.1371/journal.pcbi.1011618

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Lainscsek, C., Mendes, E.M.A.M., Salgado, G.H.O., Sejnowski, T.J. Transformations that preserve the uniqueness of the differential form for Lorenz-like systems. (2023) Chaos. 33(10). DOI: 10.1063/5.0156237

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Garcia, G.C., Gupta, K., Bartol, T.M., Sejnowski, T.J., Rangamani, P. Mitochondrial morphology governs ATP production rate. (2023) J Gen Physiol. 155(9). DOI: 10.1085/jgp.202213263

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Li, J.S., Sarma, A.A., Sejnowski, T.J., Doyle, J.C. Internal feedback in the cortical perception-action loop enables fast and accurate behavior. (2023) Proceedings of the National Academy of Sciences of the United States of America. 120(39):e2300445120. DOI: 10.1073/pnas.2300445120

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Zador, A., Escola, S., Richards, B., Ölveczky, B., Bengio, Y., Boahen, K., Botvinick, M., Chklovskii, D., Churchland, A., Clopath, C., DiCarlo, J., Ganguli, S., Hawkins, J., Körding, K., Koulakov, A., LeCun, Y., Lillicrap, T., Marblestone, A., Olshausen, B., Pouget, A., Savin, C., Sejnowski, T., Simoncelli, E., Solla, S., Sussillo, D., Tolias, A.S., Tsao, D. Catalyzing next-generation Artificial Intelligence through NeuroAI. (2023) Nature Communications. 14(1):1597. DOI: 10.1038/s41467-023-37180-x

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Sejnowski, T.J. Large Language Models and the Reverse Turing Test. (2023) Neural Computation.:1-34 DOI: 10.1162/neco_a_01563

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Bermperidis, T., Schafer, S., Gage, F.H., Sejnowski, T., Torres, E.B. Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric Disorders. (2023) Frontiers in Neuroscience. 16:884707. DOI: 10.3389/fnins.2022.884707

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Rungratsameetaweemana, N., Lainscsek, C., Cash, S.S., Garcia, J.O., Sejnowski, T.J., Bansal, K. Brain network dynamics codify heterogeneity in seizure evolution. (2022) Brain Commun. 4(5):fcac234. DOI: 10.1093/braincomms/fcac234

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Dynamic functional brain connectivity facilitates adaptive cognition and behaviour. Abnormal alterations within such connectivity could result in disrupted functions observed across various neurological conditions. As one of the most common neurological disorders, epilepsy is defined by the seemingly random occurrence of spontaneous seizures. A central but unresolved question concerns the mechanisms by which extraordinarily diverse propagation dynamics of seizures emerge. Here, we applied a graph-theoretical approach to assess dynamic reconfigurations in the functional brain connectivity before, during and after seizures that display heterogeneous propagation patterns despite sharing similar cortical onsets. We computed time-varying functional brain connectivity networks from human intracranial recordings of 67 seizures (across 14 patients) that had a focal origin-49 of these focal seizures remained focal and 18 underwent a bilateral spread (focal to bilateral tonic-clonic seizures). We utilized functional connectivity networks estimated from interictal periods across patients as control. Our results characterize network features that quantify the underlying functional dynamics associated with the observed heterogeneity of seizure propagation across these two types of focal seizures. Decoding these network features demonstrate that bilateral propagation of seizure activity is an outcome of the imbalance of global integration and segregation in the brain prior to seizure onset. We show that there exist intrinsic network signatures preceding seizure onset that are associated with the extent to which an impending seizure will propagate throughout the brain (i.e. staying within one hemisphere versus spreading transcallosally). Additionally, these features characterize an increase in segregation and a decrease in excitability within the brain network (i.e. high modularity and low spectral radius). Importantly, seizure-type-specific differences in these features emerge several minutes prior to seizure onset, suggesting the potential utility of such measures in intervention strategies. Finally, our results reveal network characteristics the onset that are unique to the propagation mechanisms of two most common focal seizure subtypes, indicative of distinct reconfiguration processes that may assist termination of each seizure type. Together, our findings provide insights into the relationship between the temporal evolution of seizure activity and the underlying functional connectivity dynamics. These results offer exciting avenues where graph-theoretical measures could potentially guide personalized clinical interventions for epilepsy and other neurological disorders in which extensive heterogeneity is observed across subtypes as well as across and within individual patients.

Chen, Y., Rosen, B.Q., Sejnowski, T.J. Dynamical differential covariance recovers directional network structure in multiscale neural systems. (2022) Proceedings of the National Academy of Sciences of the United States of America. 119(24):e2117234119. DOI: 10.1073/pnas.2117234119

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Grün, S., Li, J., McNaughton, B., Petersen, C., McCormick, D., Robson, D., Buzsáki, G., Harris, K., Sejnowski, T., Mrsic-Flogel, T., Lindén, H., Roland, P.E. Emerging principles of spacetime in brains: Meeting report on spatial neurodynamics. (2022) Neuron. 110(12):1894-1898. DOI: 10.1016/j.neuron.2022.05.018

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Chen, Y., Bukhari, Q., Lin, T.W., Sejnowski, T.J. Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times. (2022) Network neuroscience. 6(2):614-633. DOI: 10.1162/netn_a_00239

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Garcia, J.W., Bartol, T.M., Sejnowski, T.J. Multiscale modeling of presynaptic dynamics from molecular to mesoscale. (2022) PLOS Computational Biology. 18(5):e1010068. DOI: 10.1371/journal.pcbi.1010068

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Chemical synapses exhibit a diverse array of internal mechanisms that affect the dynamics of transmission efficacy. Many of these processes, such as release of neurotransmitter and vesicle recycling, depend strongly on activity-dependent influx and accumulation of Ca2+. To model how each of these processes may affect the processing of information in neural circuits, and how their dysfunction may lead to disease states, requires a computationally efficient modelling framework, capable of generating accurate phenomenology without incurring a heavy computational cost per synapse. Constructing a phenomenologically realistic model requires the precise characterization of the timing and probability of neurotransmitter release. Difficulties arise in that functional forms of instantaneous release rate can be difficult to extract from noisy data without running many thousands of trials, and in biophysical synapses, facilitation of per-vesicle release probability is confounded by depletion. To overcome this, we obtained traces of free Ca2+ concentration in response to various action potential stimulus trains from a molecular MCell model of a hippocampal Schaffer collateral axon. Ca2+ sensors were placed at varying distance from a voltage-dependent calcium channel (VDCC) cluster, and Ca2+ was buffered by calbindin. Then, using the calcium traces to drive deterministic state vector models of synaptotagmin 1 and 7 (Syt-1/7), which respectively mediate synchronous and asynchronous release in excitatory hippocampal synapses, we obtained high-resolution profiles of instantaneous release rate, to which we applied functional fits. Synchronous vesicle release occurred predominantly within half a micron of the source of spike-evoked Ca2+ influx, while asynchronous release occurred more consistently at all distances. Both fast and slow mechanisms exhibited multi-exponential release rate curves, whose magnitudes decayed exponentially with distance from the Ca2+ source. Profile parameters facilitate on different time scales according to a single, general facilitation function. These functional descriptions lay the groundwork for efficient mesoscale modelling of vesicular release dynamics.

Li, J., Pinto-Duarte, A., Zander, M., Cuoco, M.S., Lai, C.Y., Osteen, J., Fang, L., Luo, C., Lucero, J.D., Gomez-Castanon, R., Nery, J.R., Silva-Garcia, I., Pang, Y., Sejnowski, T.J., Powell, S.B., Ecker, J.R., Mukamel, E.A., Behrens, M.M. Dnmt3a knockout in excitatory neurons impairs postnatal synapse maturation and increases the repressive histone modification H3K27me3. (2022) Elife. 11. DOI: 10.7554/eLife.66909

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Zhang, Z., Cisneros, E., Lee, H.Y., Vu, J.P., Chen, Q., Benadof, C.N., Whitehill, J., Rouzbehani, R., Sy, D.T., Huang, J.S., Sejnowski, T.J., Jankovic, J., Factor, S., Goetz, C.G., Barbano, R.L., Perlmutter, J.S., Jinnah, H.A., Berman, B.D., Richardson, S.P., Stebbins, G.T., Comella, C.L., Peterson, D.A. Hold that pose: capturing cervical dystonia's head deviation severity from video. (2022) Ann Clin Transl Neurol. DOI: 10.1002/acn3.51549

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Budzinski, R.C., Nguyen, T.T., Đoàn, J., Mináč, J., Sejnowski, T.J., Muller, L.E. Geometry unites synchrony, chimeras, and waves in nonlinear oscillator networks. (2022) Chaos. 32(3):031104. DOI: 10.1063/5.0078791

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Wagner, M., Bartol, T.M., Sejnowski, T.J., Cauwenberghs, G. Markov Chain Abstractions of Electrochemical Reaction-Diffusion in Synaptic Transmission for Neuromorphic Computing. (2021) Frontiers in Neuroscience. 15:698635. DOI: 10.3389/fnins.2021.698635

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Mendelsohn, R., Garcia, G.C., Bartol, T.M., Lee, C.T., Khandelwal, P., Liu, E., Spencer, D.J., Husar, A., Bushong, E.A., Phan, S., Perkins, G., Ellisman, M.H., Skupin, A., Sejnowski, T.J., Rangamani, P. Morphological principles of neuronal mitochondria. (2021) Journal of Comparative Neurology. DOI: 10.1002/cne.25254

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Davis, Z.W., Benigno, G.B., Fletterman, C., Desbordes, T., Steward, C., Sejnowski, T.J., Reynolds, J.H., Muller, L. Spontaneous traveling waves naturally emerge from horizontal fiber time delays and travel through locally asynchronous-irregular states. (2021) Nature Communications. 12(1):6057. DOI: 10.1038/s41467-021-26175-1

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Li, Y., Kim, R., Sejnowski, T.J. Learning the Synaptic and Intrinsic Membrane Dynamics Underlying Working Memory in Spiking Neural Network Models. (2021) Neural Computation.:1-24 DOI: 10.1162/neco_a_01409

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Lainscsek, C., Cash, S.S., Sejnowski, T.J., Kurths, J. Dynamical ergodicity DDA reveals causal structure in time series. (2021) Chaos. 31(10):103108. DOI: 10.1063/5.0063724

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Hayes, T.L., Krishnan, G.P., Bazhenov, M., Siegelmann, H.T., Sejnowski, T.J., Kanan, C. Replay in Deep Learning: Current Approaches and Missing Biological Elements. (2021) Neural Computation.:1-44 DOI: 10.1162/neco_a_01433

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Nakahira, Y., Liu, Q., Sejnowski, T.J., Doyle, J.C. Diversity-enabled sweet spots in layered architectures and speed-accuracy trade-offs in sensorimotor control. (2021) Proceedings of the National Academy of Sciences of the United States of America. 118(22). DOI: 10.1073/pnas.1916367118

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Compans, B., Camus, C., Kallergi, E., Sposini, S., Martineau, M., Butler, C., Kechkar, A., Klaassen, R.V., Retailleau, N., Sejnowski, T.J., Smit, A.B., Sibarita, J.B., Bartol, T.M., Perrais, D., Nikoletopoulou, V., Choquet, D., Hosy, E. NMDAR-dependent long-term depression is associated with increased short term plasticity through autophagy mediated loss of PSD-95. (2021) Nature Communications. 12(1):2849. DOI: 10.1038/s41467-021-23133-9

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Singh, N., Bartol, T., Levine, H., Sejnowski, T., Nadkarni, S. Presynaptic endoplasmic reticulum regulates short-term plasticity in hippocampal synapses. (2021) Communications Biology. 4(1):241. DOI: 10.1038/s42003-021-01761-7

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Trujillo, C.A., Adams, J.W., Negraes, P.D., Carromeu, C., Tejwani, L., Acab, A., Tsuda, B., Thomas, C.A., Sodhi, N., Fichter, K.M., Romero, S., Zanella, F., Sejnowski, T.J., Ulrich, H., Muotri, A.R. Pharmacological reversal of synaptic and network pathology in human MECP2-KO neurons and cortical organoids. (2021) EMBO Molecular Medicine. 13(1):e12523. DOI: 10.15252/emmm.202012523

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Kim, R., Sejnowski, T.J. Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks. (2020) Nature Neuroscience. DOI: 10.1038/s41593-020-00753-w

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Tsuda, B., Tye, K.M., Siegelmann, H.T., Sejnowski, T.J. A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex. (2020) Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.2009591117

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Davis, W., Muller, L., Martinez-Trujillo, J., Sejnowski, T., Reynolds, J.H. Spontaneous travelling cortical waves gate perception in behaving primates (2020) Nature. DOI: https://www.nature.com/articles/s41586-020-2802-y.

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Sejnowski, T. Charles Gross: Mentor, Maestro, Mensch. (2020) Progress in Neurobiology.:101926 DOI: 10.1016/j.pneurobio.2020.101926

Gonzalez, C.E., Lainscsek, C., Sejnowski, T.J., Letellier, C. Assessing observability of chaotic systems using Delay Differential Analysis. (2020) Chaos. 30(10):103113. DOI: 10.1063/5.0015533

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Sobczak, F., He, Y., Sejnowski, T.J., Yu, X. Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics. (2020) Cerebral Cortex . DOI: 10.1093/cercor/bhaa260

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Abbott, L.F., Bock, D.D., Callaway, E.M., Denk, W., Dulac, C., Fairhall, A.L., Fiete, I., Harris, K.M., Helmstaedter, M., Jain, V., Kasthuri, N., LeCun, Y., Lichtman, J.W., Littlewood, P.B., Luo, L., Maunsell, J.H.R., Reid, R.C., Rosen, B.R., Rubin, G.M., Sejnowski, T.J., Seung, H.S., Svoboda, K., Tank, D.W., Tsao, D., Van Essen, D.C. The Mind of a Mouse. (2020) Cell. 182(6):1372-1376. DOI: 10.1016/j.cell.2020.08.010

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Lin, T.W., Chen, Y., Bukhari, Q., Krishnan, G.P., Bazhenov, M., Sejnowski, T.J. Differential Covariance: A New Method to Estimate Functional Connectivity in fMRI. (2020) Neural Computation.:1-33 DOI: 10.1162/neco_a_01323

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Ordyan, M., Bartol, T., Kennedy, M., Rangamani, P., Sejnowski, T. Interactions between calmodulin and neurogranin govern the dynamics of CaMKII as a leaky integrator. (2020) PLOS Computational Biology. 16(7):e1008015. DOI: 10.1371/journal.pcbi.1008015

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Calmodulin-dependent kinase II (CaMKII) has long been known to play an important role in learning and memory as well as long term potentiation (LTP). More recently it has been suggested that it might be involved in the time averaging of synaptic signals, which can then lead to the high precision of information stored at a single synapse. However, the role of the scaffolding molecule, neurogranin (Ng), in governing the dynamics of CaMKII is not yet fully understood. In this work, we adopt a rule-based modeling approach through the Monte Carlo method to study the effect of Ca2+ signals on the dynamics of CaMKII phosphorylation in the postsynaptic density (PSD). Calcium surges are observed in synaptic spines during an EPSP and back-propagating action potential due to the opening of NMDA receptors and voltage dependent calcium channels. Using agent-based models, we computationally investigate the dynamics of phosphorylation of CaMKII monomers and dodecameric holoenzymes. The scaffolding molecule, Ng, when present in significant concentration, limits the availability of free calmodulin (CaM), the protein which activates CaMKII in the presence of calcium. We show that Ng plays an important modulatory role in CaMKII phosphorylation following a surge of high calcium concentration. We find a non-intuitive dependence of this effect on CaM concentration that results from the different affinities of CaM for CaMKII depending on the number of calcium ions bound to the former. It has been shown previously that in the absence of phosphatase, CaMKII monomers integrate over Ca2+ signals of certain frequencies through autophosphorylation (Pepke et al, Plos Comp. Bio., 2010). We also study the effect of multiple calcium spikes on CaMKII holoenzyme autophosphorylation, and show that in the presence of phosphatase, CaMKII behaves as a leaky integrator of calcium signals, a result that has been recently observed in vivo. Our models predict that the parameters of this leaky integrator are finely tuned through the interactions of Ng, CaM, CaMKII, and PP1, providing a mechanism to precisely control the sensitivity of synapses to calcium signals. Author Summary not valid for PLOS ONE submissions.

Goncalves, J., Bartol, T.M., Camus, C., Levet, F., Menegolla, A.P., Sejnowski, T.J., Sibarita, J.B., Vivaudou, M., Choquet, D., Hosy, E. Nanoscale co-organization and coactivation of AMPAR, NMDAR, and mGluR at excitatory synapses. (2020) Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.1922563117

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Filimon, F., Nelson, J.D., Sejnowski, T.J., Sereno, M.I., Cottrell, G.W. The ventral striatum dissociates information expectation, reward anticipation, and reward receipt. (2020) Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.1911778117

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DAS, A., Cash, S.S., Sejnowski, T.J. Heterogeneity of Preictal Dynamics in Human Epileptic Seizures. (2020) IEEE Access. 8:52738-52748. DOI: 10.1109/access.2020.2981017

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It is generally understood that there is a preictal phase in the development of a seizure and this precictal period is the basis for seizure prediction attempts. The focus of this study is the preictal global spatiotemporal dynamics and its intra-patient variability. We analyzed preictal broadband brain connectivity from human electrocorticography (ECoG) recordings of 185 seizures (which included 116 clinical seizures) collected from 12 patients. ECoG electrodes record from only a part of the cortex, leaving large regions of the brain unobserved. Brain connectivity was therefore estimated using the sparse-plus-latent-regularized precision matrix (SLRPM) method, which calculates connectivity from partial correlations of the conditional statistics of the observed regions given the unobserved latent regions. Brain connectivity was quantified using eigenvector centrality (EC), from which a degree of heterogeneity was calculated for the preictal periods of all seizures in each patient. Results from the SLRPM method are compared to those from the sparse-regularized precision matrix (SRPM) and correlation methods, which do not account for the unobserved inputs when estimating brain connectivity. The degree of heterogeneity estimated by the SLRPM method is higher than those estimated by the SRPM and correlation methods for the preictal periods in most patients. These results reveal substantial heterogeneity or desynchronization among brain areas in the preictal period of human epileptic seizures. Furthermore, the SLRPM method identifies more onset channels from the preictal active electrodes compared to the SRPM and correlation methods. Finally, the correlation between the degree of heterogeneity and seizure severity of patients for SLRPM and SRPM methods were lower than that obtained from the correlation method. These results support recent findings suggesting that inhibitory neurons can have anti-seizure effects by inducing variability or heterogeneity across seizures. Understanding how this variability is linked to seizure initiation may lead to better predictions and controlling therapies.

Sejnowski, T.J. The unreasonable effectiveness of deep learning in artificial intelligence. (2020) Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.1907373117

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Garcia, G.C., Bartol, T.M., Phan, S., Bushong, E.A., Perkins, G., Sejnowski, T.J., Ellisman, M.H., Skupin, A. Mitochondrial morphology provides a mechanism for energy buffering at synapses. (2019) Scientific Reports. 9(1):18306. DOI: 10.1038/s41598-019-54159-1

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Jullié, D., Stoeber, M., Sibarita, J.B., Zieger, H.L., Bartol, T.M., Arttamangkul, S., Sejnowski, T.J., Hosy, E., von Zastrow, M. A Discrete Presynaptic Vesicle Cycle for Neuromodulator Receptors. (2019) Neuron. DOI: 10.1016/j.neuron.2019.11.016

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Pharris, M.C., Patel, N.M., VanDyk, T.G., Bartol, T.M., Sejnowski, T.J., Kennedy, M.B., Stefan, M.I., Kinzer-Ursem, T.L. A Multi-State Model of the CaMKII Dodecamer Suggests a Role for Calmodulin in Maintenance of Autophosphorylation. (2019) PLOS Computational Biology. 15(12):e1006941. DOI: 10.1371/journal.pcbi.1006941

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Lainscsek, C., Rungratsameetaweemana, N., Cash, S.S., Sejnowski, T.J. Cortical chimera states predict epileptic seizures. (2019) Chaos. 29(12):121106. DOI: 10.1063/1.5139654

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Ruiz-Martínez, , Bartol, T.M., Sejnowski, T.J., Tartakovsky, D.M. Stochastic self-tuning hybrid algorithm for reaction-diffusion systems. (2019) Journal of Chemical Physics. 151(24):244117. DOI: 10.1063/1.5125022

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Siddharth, S., Jung, T.P., Sejnowski, T.J. Impact of Affective Multimedia Content on the Electroencephalogram and Facial Expressions. (2019) Scientific Reports. 9(1):16295. DOI: 10.1038/s41598-019-52891-2

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De la Fuente, I.M., Bringas, C., Malaina, I., Regner, B., Pérez-Samartín, A., Boyano, M.D., Fedetz, M., López, J.I., Pérez-Yarza, G., Cortes, J.M., Sejnowski, T. The nucleus does not significantly affect the migratory trajectories of amoeba in two-dimensional environments. (2019) Scientific Reports. 9(1):16369. DOI: 10.1038/s41598-019-52716-2

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Litvina, E., Adams, A., Barth, A., Bruchez, M., Carson, J., Chung, J.E., Dupre, K.B., Frank, L.M., Gates, K.M., Harris, K.M., Joo, H., William Lichtman, J., Ramos, K.M., Sejnowski, T., Trimmer, J.S., White, S., Koroshetz, W. BRAIN Initiative: Cutting-Edge Tools and Resources for the Community. (2019) Journal of Neuroscience. 39(42):8275-8284. DOI: 10.1523/JNEUROSCI.1169-19.2019

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The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance the understanding of healthy and diseased brain circuit function through technological innovation. Core principles for this goal include the validation and dissemination of the myriad innovative technologies, tools, methods, and resources emerging from BRAIN-funded research. Innovators, BRAIN funding agencies, and non-Federal partners are working together to develop strategies for making these products usable, available, and accessible to the scientific community. Here, we describe several early strategies for supporting the dissemination of BRAIN technologies. We aim to invigorate a dialogue with the neuroscience research and funding community, interdisciplinary collaborators, and trainees about the existing and future opportunities for cultivating groundbreaking research products into mature, integrated, and adaptable research systems. Along with the accompanying Society for Neuroscience 2019 Mini-Symposium, "BRAIN Initiative: Cutting-Edge Tools and Resources for the Community," we spotlight the work of several BRAIN investigator teams who are making progress toward providing tools, technologies, and services for the neuroscience community. These tools access neural circuits at multiple levels of analysis, from subcellular composition to brain-wide network connectivity, including the following: integrated systems for EM- and florescence-based connectomics, advances in immunolabeling capabilities, and resources for recording and analyzing functional connectivity. Investigators describe how the resources they provide to the community will contribute to achieving the goals of the NIH BRAIN Initiative. Finally, in addition to celebrating the contributions of these BRAIN-funded investigators, the Mini-Symposium will illustrate the broader diversity of BRAIN Initiative investments in cutting-edge technologies and resources.

Kim, R., Li, Y., Sejnowski, T.J. Simple framework for constructing functional spiking recurrent neural networks. (2019) Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.1905926116

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Lainscsek, C., Gonzalez, C.E., Sampson, A.L., Cash, S.S., Sejnowski, T.J. Causality detection in cortical seizure dynamics using cross-dynamical delay differential analysis. (2019) Chaos. 29(10):101103. DOI: 10.1063/1.5126125

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Cugno, A., Bartol, T.M., Sejnowski, T.J., Iyengar, R., Rangamani, P. Geometric principles of second messenger dynamics in dendritic spines. (2019) Scientific Reports. 9(1):11676. DOI: 10.1038/s41598-019-48028-0

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Wang, H.P., Garcia, J.W., Sabottke, C.F., Spencer, D., Sejnowski, T.J. Feedforward thalamocortical connectivity preserves stimulus timing information in sensory pathways. (2019) Journal of Neuroscience. DOI: 10.1523/JNEUROSCI.3165-17.2019

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Reliable timing of cortical spikes in response to visual events is crucial in representing visual inputs to the brain. Spikes in the primary visual cortex need to occur at the same time within a repeated visual stimulus. Two classical mechanisms are employed by the cortex to enhance reliable timing. First, cortical neurons respond reliably to a restricted set of stimuli through their preference for certain patterns of membrane potential due to their intrinsic properties. Second, intracortical networking of excitatory and inhibitory neurons induces lateral inhibition that, through the timing and strength of IPSCs and EPSCs, produces sparse and reliably timed cortical neuron spike trains to be transmitted downstream. Here we describe a third mechanism that, through preferential thalamocortical synaptic connectivity, enhances the trial-to-trial timing precision of cortical spikes in the presence of spike train variability within each trial that is introduced between LGN neurons in the retino-thalamic pathway. Applying experimentally recorded LGN spike trains from the anesthetized cat to a detailed model of a spiny stellate V1 neuron, we found that output spike timing precision improved with increasing numbers of convergent LGN inputs. The improvement was consistent with the predicted proportionality of √???? for ???? LGN source neurons. We also found connectivity configurations that maximize reliability and that generate V1 cell output spike trains quantitatively similar to the experimental recordings. Our findings suggest a general principle, namely intra-trial variability among converging inputs, that increases stimulus response precision and is widely applicable to synaptically connected spiking neurons.The early visual pathway of the cat is favorable for studying the effects of trial-to-trial variability of synaptic inputs and intra-trial variability of thalamocortical connectivity on information transmission into the visual cortex. We have used a detailed model to show that there are preferred combinations of the number of thalamic afferents and the number of synapses per afferent that maximize the output reliability and spike-timing precision of cortical neurons. This provides additional insights into how synchrony in thalamic spike trains can reduce trial-to-trial variability to produce highly reliable reporting of sensory events to the cortex. The same principles may apply to other converging pathways where temporally jittered spike trains can reliably drive the downstream neuron and improve temporal precision.

Bell, M., Bartol, T., Sejnowski, T., Rangamani, P. Dendritic spine geometry and spine apparatus organization govern the spatiotemporal dynamics of calcium. (2019) J. Gen. Physiol. DOI: 10.1085/jgp.201812261

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Pinto-Duarte, A., Roberts, A.J., Ouyang, K., Sejnowski, T.J. Impairments in remote memory caused by the lack of Type 2 IP receptors. (2019) Glia. DOI: 10.1002/glia.23679

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Zhou, J., Ma, J., Chen, Y., Cheng, C., Bao, B., Peng, J., Sejnowski, T.J., Dixon, J.R., Ecker, J.R. Robust single-cell Hi-C clustering by convolution- and random-walk-based imputation. (2019) Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.1901423116

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Oakley, B.A., Sejnowski, T.J. What we learned from creating one of the world's most popular MOOCs. (2019) NPJ Sci Learn. 4:7. DOI: 10.1038/s41539-019-0046-0

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Ernst, O.K., Bartol, T.M., Sejnowski, T.J., Mjolsness, E. Learning moment closure in reaction-diffusion systems with spatial dynamic Boltzmann distributions. (2019) Phys Rev E. 99(6-1):063315. DOI: 10.1103/PhysRevE.99.063315

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Das, A., Sexton, D., Lainscsek, C., Cash, S.S., Sejnowski, T.J. Characterizing Brain Connectivity from Human Electrocorticography Recordings with Unobserved Inputs during Epileptic Seizures. (2019) Neural Computation.:1-56 DOI: 10.1162/neco_a_01205

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Epilepsy is a neurological disorder characterized by the sudden occurrence of unprovoked seizures. There is extensive evidence of significantly altered brain connectivity during seizure periods in the human brain. Research on analyzing human brain functional connectivity during epileptic seizures has been limited predominantly to the use of the correlation method. However, spurious connectivity can be measured between two brain regions without having direct connection or interaction between them. Correlations can be due to the apparent interactions of the two brain regions resulting from common input from a third region, which may or may not be observed. Hence, researchers have recently proposed a sparse-plus-latent-regularized precision matrix (SLRPM) when there are unobserved or latent regions interacting with the observed regions. The SLRPM method yields partial correlations of the conditional statistics of the observed regions given the latent regions, thus identifying observed regions that are conditionally independent of both the observed and latent regions. We evaluate the performance of the methods using a spring-mass artificial network and assuming that some nodes cannot be observed, thus constituting the latent variables in the example. Several cases have been considered, including both sparse and dense connections, short-range and long-range connections, and a varying number of latent variables. The SLRPM method is then applied to estimate brain connectivity during epileptic seizures from human ECoG recordings. Seventy-four clinical seizures from five patients, all having complex partial epilepsy, were analyzed using SLRPM, and brain connectivity was quantified using modularity index, clustering coefficient, and eigenvector centrality. Furthermore, using a measure of latent inputs estimated by the SLRPM method, it was possible to automatically detect 72 of the 74 seizures with four false positives and find six seizures that were not marked manually.

Siddharth, Patel, A.N., Jung, T.P., Sejnowski, T.J. A Wearable Multi-Modal Bio-Sensing System Towards Real-World Applications. (2019) IEEE Trans Biomed Eng. 66(4):1137-1147. DOI: 10.1109/TBME.2018.2868759

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Tapia, J.J., Saglam, A.S., Czech, J., Kuczewski, R., Bartol, T.M., Sejnowski, T.J., Faeder, J.R. MCell-R: A Particle-Resolution Network-Free Spatial Modeling Framework. (2019) Methods in Molecular Biology. 1945:203-229. DOI: 10.1007/978-1-4939-9102-0_9

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Aimon, S., Katsuki, T., Jia, T., Grosenick, L., Broxton, M., Deisseroth, K., Sejnowski, T.J., Greenspan, R.J. Fast near-whole-brain imaging in adult Drosophila during responses to stimuli and behavior. (2019) PLOS Biology. 17(2):e2006732. DOI: 10.1371/journal.pbio.2006732

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Lainscsek, C., Sampson, A.L., Kim, R., Thomas, M.L., Man, K., Lainscsek, X., , Swerdlow, N.R., Braff, D.L., Sejnowski, T.J., Light, G.A. Nonlinear dynamics underlying sensory processing dysfunction in schizophrenia. (2019) Proceedings of the National Academy of Sciences of the United States of America. 116(9):3847-3852. DOI: 10.1073/pnas.1810572116

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Padmanabhan, K., Osakada, F., Tarabrina, A., Kizer, E., Callaway, E.M., Gage, F.H., Sejnowski, T.J. Centrifugal Inputs to the Main Olfactory Bulb Revealed Through Whole Brain Circuit-Mapping. (2019) Frontiers in Neuroanatomy. 12:115. DOI: 10.3389/fnana.2018.00115

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Sampson, A.L., Lainscsek, C., Gonzalez, C.E., Ulbert, I., Devinsky, O., Fabó, D., Halgren, E., Cash, S.S., Sejnowski, T.J. Delay Differential Analysis for Dynamical Sleep Spindle Detection. (2019) Journal of Neuroscience Methods. DOI: 10.1016/j.jneumeth.2019.01.009

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Reddy, G., Wong-Ng, J., Celani, A., Sejnowski, T.J., Vergassola, M. Glider soaring via reinforcement learning in the field. (2018) Nature. DOI: 10.1038/s41586-018-0533-0

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Sejnowski, T. The prophet who foretold our future. (2018) Brain : A Journal of Neurology. 141(9):2820-2822. DOI: 10.1093/brain/awy212

Ernst, O.K., Bartol, T., Sejnowski, T., Mjolsness, E. Learning dynamic Boltzmann distributions as reduced models of spatial chemical kinetics. (2018) J Chem Phys. 149(3):034107. DOI: 10.1063/1.5026403

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Haas, K.T., Compans, B., Letellier, M., Bartol, T.M., Grillo-Bosch, D., Sejnowski, T.J., Sainlos, M., Choquet, D., Thoumine, O., Hosy, E. Pre-post synaptic alignment through neuroligin-1 tunes synaptic transmission efficiency. (2018) Elife. 7. DOI: 10.7554/eLife.31755

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Siddharth, Jung, T.P., Sejnowski, T.J. Multi-modal Approach for Affective Computing. (2018) Conf Proc IEEE Eng Med Biol Soc. 2018:291-294. DOI: 10.1109/EMBC.2018.8512320

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Krishnan, G.P., Rosen, B.Q., Chen, J.Y., Muller, L., Sejnowski, T.J., Cash, S.S., Halgren, E., Bazhenov, M. Thalamocortical and intracortical laminar connectivity determines sleep spindle properties. (2018) PLOS Computational Biology. 14(6):e1006171. DOI: 10.1371/journal.pcbi.1006171

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Ruiz-Martínez, , Bartol, T.M., Sejnowski, T.J., Tartakovsky, D.M. Efficient models of polymerization applied to FtsZ ring assembly in . (2018) Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.1719391115

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Moldakarimov, S., Bazhenov, M., Feldman, D.E., Sejnowski, T.J. Structured networks support sparse traveling waves in rodent somatosensory cortex. (2018) Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.1710202115

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Wang, X., Pinto-Duarte, A., Behrens, M.M., Zhou, X., Sejnowski, T.J. Ketamine independently modulated power and phase-coupling of theta oscillations in Sp4 hypomorphic mice. (2018) PLOS One. 13(3):e0193446. DOI: 10.1371/journal.pone.0193446

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Reduced expression of Sp4, the murine homolog of human SP4, a risk gene of multiple psychiatric disorders, led to N-methyl-D-aspartate (NMDA) hypofunction in mice, producing behavioral phenotypes reminiscent of schizophrenia, including hypersensitivity to ketamine. As accumulating evidence on molecular mechanisms and behavioral phenotypes established Sp4 hypomorphism as a promising animal model, systems-level neural circuit mechanisms of Sp4 hypomorphism, especially network dynamics underlying cognitive functions, remain poorly understood. We attempted to close this gap in knowledge in the present study by recording multi-channel epidural electroencephalogram (EEG) from awake behaving wildtype and Sp4 hypomorphic mice. We characterized cortical theta-band power and phase-coupling phenotypes, a known neural circuit substrate underlying cognitive functions, and further studied the effects of a subanesthetic dosage of ketamine on theta abnormalities unique to Sp4 hypomorphism. Sp4 hypomorphic mice had markedly elevated theta power localized frontally and parietally, a more pronounced theta phase progression along the neuraxis, and a stronger frontal-parietal theta coupling. Acute subanesthetic ketamine did not affect theta power in wildtype animals but significantly reduced it in Sp4 hypomorphic mice, nearly completely neutralizing their excessive frontal/parietal theta power. Ketamine did not significantly alter cortical theta phase progression in either wildtype or Sp4 hypomorphic animals, but significantly strengthened cortical theta phase-coupling in wildtype, but not in Sp4 hypomorphic animals. Our results suggested that the resting-state phenotypes of cortical theta oscillations unique to Sp4 hypomorphic mice closely mimicked a schizophrenic endophenotype. Further, ketamine independently modulated Sp4 hypomorphic anomalies in theta power and phase-coupling, suggesting separate underlying neural circuit mechanisms.

Muller, L., Chavane, F., Reynolds, J., Sejnowski, T.J. Cortical travelling waves: mechanisms and computational principles. (2018) Nature Reviews Neuroscience. DOI: 10.1038/nrn.2018.20

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Kaya, C., Cheng, M.H., Block, E.R., Bartol, T.M., Sejnowski, T.J., Sorkin, A., Faeder, J.R., Bahar, I. Heterogeneities in Axonal Structure and Transporter Distribution Lower Dopamine Reuptake Efficiency. (2018) eNeuro. 5(1). DOI: 10.1523/ENEURO.0298-17.2017

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Bromer, C., Bartol, T.M., Bowden, J.B., Hubbard, D.D., Hanka, D.C., Gonzalez, P.V., Kuwajima, M., Mendenhall, J.M., Parker, P.H., Abraham, W.C., Sejnowski, T.J., Harris, K.M. Long-term potentiation expands information content of hippocampal dentate gyrus synapses. (2018) Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.1716189115

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Wang, M., He, Y., Sejnowski, T.J., Yu, X. Brain-state dependent astrocytic Ca2+ signals are coupled to both positive and negative BOLD-fMRI signals. (2018) Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.1711692115

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O'Donnell, C., Gonçalves, J.T., Portera-Cailliau, C., Sejnowski, T.J. Beyond excitation/inhibition imbalance in multidimensional models of neural circuit changes in brain disorders. (2017) Elife. 6. DOI: 10.7554/eLife.26724

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Li, Y., Wang, F., Chen, Y., Cichocki, A., Sejnowski, T. The Effects of Audiovisual Inputs on Solving the Cocktail Party Problem in the Human Brain: An fMRI Study. (2017) Cerebral Cortex .:1-15 DOI: 10.1093/cercor/bhx235

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Holter, K.E., Kehlet, B., Devor, A., Sejnowski, T.J., Dale, A.M., Omholt, S.W., Ottersen, O.P., Nagelhus, E.A., Mardal, K.A., Pettersen, K.H. Interstitial solute transport in 3D reconstructed neuropil occurs by diffusion rather than bulk flow. (2017) Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.1706942114

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Luo, C., Keown, C.L., Kurihara, L., Zhou, J., He, Y., Li, J., Castanon, R., Lucero, J., Nery, J.R., Sandoval, J.P., Bui, B., Sejnowski, T.J., Harkins, T.T., Mukamel, E.A.*, Behrens, M.M.*, Ecker, J.R.* Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. (2017) Science. 357(6351):600-604. DOI: 10.1126/science.aan3351

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Lainscsek, C., Weyhenmeyer, J., Cash, S.S., Sejnowski, T.J. Delay Differential Analysis of Seizures in Multichannel Electrocorticography Data. (2017) Neural Computation.:1-38 DOI: 10.1162/neco_a_01009

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Lin, T.W., Das, A., Krishnan, G.P., Bazhenov, M., Sejnowski, T.J. Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings. (2017) Neural Computation.:1-52 DOI: 10.1162/neco_a_01008

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Lainscsek, C., Muller, L.E., Sampson, A.L., Sejnowski, T.J. Analytical Derivation of Nonlinear Spectral Effects and 1/f Scaling Artifact in Signal Processing of Real-World Data. (2017) Neural Computation. 29(7):2004-2020. DOI: 10.1162/NECO_a_00979

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Veit, J., Hakim, R., Jadi, M.P., Sejnowski, T.J., Adesnik, H. Cortical gamma band synchronization through somatostatin interneurons. (2017) Nature Neuroscience. 20(7):951-959. DOI: 10.1038/nn.4562

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Hurtado-López, J., Ramirez-Moreno, D.F., Sejnowski, T.J. Decision-making neural circuits mediating social behaviors : An attractor network model. (2017) Journal of Computational Neuroscience. DOI: 10.1007/s10827-017-0654-8

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Peterson, D.A., Sejnowski, T.J. A Dynamic Circuit Hypothesis for the Pathogenesis of Blepharospasm. (2017) Frontiers in Computational Neuroscience. 11:11. DOI: 10.3389/fncom.2017.00011

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Blepharospasm (sometimes called "benign essential blepharospasm," BEB) is one of the most common focal dystonias. It involves involuntary eyelid spasms, eye closure, and increased blinking. Despite the success of botulinum toxin injections and, in some cases, pharmacologic or surgical interventions, BEB treatments are not completely efficacious and only symptomatic. We could develop principled strategies for preventing and reversing the disease if we knew the pathogenesis of primary BEB. The objective of this study was to develop a conceptual framework and dynamic circuit hypothesis for the pathogenesis of BEB. The framework extends our overarching theory for the multifactorial pathogenesis of focal dystonias (Peterson et al., 2010) to incorporate a two-hit rodent model specifically of BEB (Schicatano et al., 1997). We incorporate in the framework three features critical to cranial motor control: (1) the joint influence of motor cortical regions and direct descending projections from one of the basal ganglia output nuclei, the substantia nigra pars reticulata, on brainstem motor nuclei, (2) nested loops composed of the trigeminal blink reflex arc and the long sensorimotor loop from trigeminal nucleus through thalamus to somatosensory cortex back through basal ganglia to the same brainstem nuclei modulating the reflex arc, and (3) abnormalities in the basal ganglia dopamine system that provide a sensorimotor learning substrate which, when combined with patterns of increased blinking, leads to abnormal sensorimotor mappings manifest as BEB. The framework explains experimental data on the trigeminal reflex blink excitability (TRBE) from Schicatano et al. and makes predictions that can be tested in new experimental animal models based on emerging genetics in dystonia, including the recently characterized striatal-specific D1R dopamine transduction alterations caused by the GNAL mutation. More broadly, the model will provide a guide for future efforts to mechanistically link multiple factors in the pathogenesis of BEB and facilitate simulations of how exogenous manipulations of the pathogenic factors could ultimately be used to prevent and reverse the disorder.

Das, A., Sampson, A.L., Lainscsek, C., Muller, L., Lin, W., Doyle, J.C., Cash, S.S., Halgren, E., Sejnowski, T.J. Interpretation of the Precision Matrix and Its Application in Estimating Sparse Brain Connectivity during Sleep Spindles from Human Electrocorticography Recordings. (2017) Neural Computation. 29(3):603-642. DOI: 10.1162/NECO_a_00936

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O'Donnell, C., Gonçalves, J.T., Whiteley, N., Portera-Cailliau, C., Sejnowski, T.J. The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data. (2017) Neural Computation. 29(1):50-93. DOI: 10.1162/NECO_a_00910

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Williams, A.H., O'Donnell, C., Sejnowski, T.J., O'Leary, T. Dendritic trafficking faces physiologically critical speed-precision tradeoffs. (2016) Elife. 5. DOI: 10.7554/eLife.20556

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Muller, L., Piantoni, G., Koller, D., Cash, S.S., Halgren, E., Sejnowski, T.J. Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night. (2016) Elife. 5. DOI: 10.7554/eLife.17267

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Peterson, D.A., Littlewort, G.C., Bartlett, M.S., Macerollo, A., Perlmutter, J.S., Jinnah, H.A., Hallett, M., Sejnowski, T.J. Objective, computerized video-based rating of blepharospasm severity. (2016) Neurology. 87(20):2146-2153. DOI: 10.1212/WNL.0000000000003336

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Churchland, P.S., Sejnowski, T.J. Blending computational and experimental neuroscience. (2016) Nature Reviews Neuroscience. 17(11):667-668. DOI: 10.1038/nrn.2016.114

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Grillner, S., Ip, N., Koch, C., Koroshetz, W., Okano, H., Polachek, M., Poo, M.M., Sejnowski, T.J. Worldwide initiatives to advance brain research. (2016) Nature Neuroscience. 19(9):1118-22. DOI: 10.1038/nn.4371

Kosik, K.S., Sejnowski, T.J., Raichle, M.E., Ciechanover, A., Baltimore, D. A path toward understanding neurodegeneration. (2016) Science. 353(6302):872-3. DOI: 10.1126/science.aai7622

Reddy, G., Celani, A., Sejnowski, T.J., Vergassola, M. Learning to soar in turbulent environments. (2016) Proceedings of the National Academy of Sciences of the United States of America. 113(33):E4877-84. DOI: 10.1073/pnas.1606075113

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Huh, D., Sejnowski, T.J. Conservation law for self-paced movements. (2016) Proceedings of the National Academy of Sciences of the United States of America. 113(31):8831-6. DOI: 10.1073/pnas.1608724113

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Padmanabhan, K., Osakada, F., Tarabrina, A., Kizer, E., Callaway, E.M., Gage, F.H., Sejnowski, T.J. Diverse Representations of Olfactory Information in Centrifugal Feedback Projections. (2016) Journal of Neuroscience. 36(28):7535-45. DOI: 10.1523/JNEUROSCI.3358-15.2016

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Ruiz-Martinez, A., Bartol, T.M., Sejnowski, T.J., Tartakovsky, D.M. Efficient Multiscale Models of Polymer Assembly. (2016) Biophysical Journal. 111(1):185-96. DOI: 10.1016/j.bpj.2016.05.022

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Jadi, M.P., Behrens, M.M., Sejnowski, T.J. Abnormal Gamma Oscillations in N-Methyl-D-Aspartate Receptor Hypofunction Models of Schizophrenia. (2016) Biological Psychiatry. 79(9):716-26. DOI: 10.1016/j.biopsych.2015.07.005

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Saremi, S., Sejnowski, T.J. Correlated Percolation, Fractal Structures, and Scale-Invariant Distribution of Clusters in Natural Images. (2016) IEEE Trans Pattern Anal Mach Intell. 38(5):1016-20. DOI: 10.1109/TPAMI.2015.2481402

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Rodrigues, S., Desroches, M., Krupa, M., Cortes, J.M., Sejnowski, T.J., Ali, A.B. Time-coded neurotransmitter release at excitatory and inhibitory synapses. (2016) Proceedings of the National Academy of Sciences of the United States of America. 113(8):E1108-15. DOI: 10.1073/pnas.1525591113

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O'Donnell, C., Sejnowski, T.J. Street View of the Cognitive Map. (2016) Cell. 164(1-2):13-5. DOI: 10.1016/j.cell.2015.12.051

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Veltz, R., Sejnowski, T.J. Periodic Forcing of Inhibition-Stabilized Networks: Nonlinear Resonances and Phase-Amplitude Coupling. (2015) Neural Computation. 27(12):2477-509. DOI: 10.1162/NECO_a_00786

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Bartol, T.M., Bromer, C., Kinney, J., Chirillo, M.A., Bourne, J.N., Harris, K.M., Sejnowski, T.J. Nanoconnectomic upper bound on the variability of synaptic plasticity. (2015) Elife. 4:e10778. DOI: 10.7554/eLife.10778

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Bartol, T.M., Keller, D.X., Kinney, J.P., Bajaj, C.L., Harris, K.M., Sejnowski, T.J., Kennedy, M.B. Computational reconstitution of spine calcium transients from individual proteins. (2015) Front Synaptic Neurosci. 7:17. DOI: 10.3389/fnsyn.2015.00017

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Wang, X., Pinto-Duarte, A., Behrens, M.M., Zhou, X., Sejnowski, T.J. Characterization of spatio-temporal epidural event-related potentials for mouse models of psychiatric disorders. (2015) Scientific Reports. 5:14964. DOI: 10.1038/srep14964

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Barnes, S.A., Pinto-Duarte, A., Kappe, A., Zembrzycki, A., Metzler, A., Mukamel, E.A., Lucero, J., Wang, X., Sejnowski, T.J., Markou, A.*, Behrens, M.M.* Disruption of mGluR5 in parvalbumin-positive interneurons induces core features of neurodevelopmental disorders. (2015) Molecular Psychiatry. 20(10):1161-72. DOI: 10.1038/mp.2015.113

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González, O.C., Krishnan, G.P., Chauvette, S., Timofeev, I., Sejnowski, T., Bazhenov, M. Modeling of Age-Dependent Epileptogenesis by Differential Homeostatic Synaptic Scaling. (2015) Journal of Neuroscience. 35(39):13448-62. DOI: 10.1523/JNEUROSCI.5038-14.2015

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Coggan, J.S., Sejnowski, T.J., Prescott, S.A. Cooperativity between remote sites of ectopic spiking allows afterdischarge to be initiated and maintained at different locations. (2015) Journal of Computational Neuroscience. 39(1):17-28. DOI: 10.1007/s10827-015-0562-8

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Huh, D., Sejnowski, T.J. Spectrum of power laws for curved hand movements. (2015) Proceedings of the National Academy of Sciences of the United States of America. 112(29):E3950-8. DOI: 10.1073/pnas.1510208112

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Schultz, M.D., He, Y., Whitaker, J.W., Hariharan, M., Mukamel, E.A., Leung, D., Rajagopal, N., Nery, J.R., Urich, M.A., Chen, H., Lin, S., Lin, Y., Jung, I., Schmitt, A.D., Selvaraj, S., Ren, B., Sejnowski, T.J., Wang, W., Ecker, J.R. Human body epigenome maps reveal noncanonical DNA methylation variation. (2015) Nature. 523(7559):212-6. DOI: 10.1038/nature14465

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Lainscsek, C., Weyhenmeyer, J., Sejnowski, T.J., Letellier, C. Discovering independent parameters in complex dynamical systems. (2015) Chaos Solitons Fractals. 76:182-189. DOI: 10.1016/j.chaos.2015.04.001

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Mo, A., Mukamel, E.A., Davis, F.P., Luo, C., Henry, G.L., Picard, S., Urich, M.A., Nery, J.R., Sejnowski, T.J., Lister, R., Eddy, S.R., Ecker, J.R., Nathans, J. Epigenomic Signatures of Neuronal Diversity in the Mammalian Brain. (2015) Neuron. 86(6):1369-84. DOI: 10.1016/j.neuron.2015.05.018

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Johnson, T., Bartol, T., Sejnowski, T., Mjolsness, E. Model reduction for stochastic CaMKII reaction kinetics in synapses by graph-constrained correlation dynamics. (2015) Phys Biol. 12(4):045005. DOI: 10.1088/1478-3975/12/4/045005

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Sejnowski, T.J. Vernon Mountcastle: Father of neuroscience. (2015) Proceedings of the National Academy of Sciences of the United States of America. 112(21):6523-4. DOI: 10.1073/pnas.1506644112

Izpisua Belmonte, J.C., Callaway, E.M., Caddick, S.J., Churchland, P., Feng, G., Homanics, G.E., Lee, K.F., Leopold, D.A., Miller, C.T., Mitchell, J.F., Mitalipov, S., Moutri, A.R., Movshon, J.A., Okano, H., Reynolds, J.H., Ringach, D., Sejnowski, T.J., Silva, A.C., Strick, P.L., Wu, J., Zhang, F. Brains, genes, and primates. (2015) Neuron. 86(3):617-31. DOI: 10.1016/j.neuron.2015.03.021

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Jorgenson, L.A., Newsome, W.T., Anderson, D.J., Bargmann, C.I., Brown, E.N., Deisseroth, K., Donoghue, J.P., Hudson, K.L., Ling, G.S., MacLeish, P.R., Marder, E., Normann, R.A., Sanes, J.R., Schnitzer, M.J., Sejnowski, T.J., Tank, D.W., Tsien, R.Y., Ugurbil, K., Wingfield, J.C. The BRAIN Initiative: developing technology to catalyse neuroscience discovery. (2015) Philos. Trans. R. Soc. Lond., B, Biol. Sci. 370(1668). DOI: 10.1098/rstb.2014.0164

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Lainscsek, C., Sejnowski, T.J. Delay differential analysis of time series. (2015) Neural Computation. 27(3):594-614. DOI: 10.1162/NECO_a_00706

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Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time compared with frequency-based methods such as the DFT and cross-spectral analysis.

Lainscsek, C., Hernandez, M.E., Poizner, H., Sejnowski, T.J. Delay differential analysis of electroencephalographic data. (2015) Neural Computation. 27(3):615-27. DOI: 10.1162/NECO_a_00656

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Moldakarimov, S., Bazhenov, M., Sejnowski, T.J. Feedback stabilizes propagation of synchronous spiking in cortical neural networks. (2015) Proceedings of the National Academy of Sciences of the United States of America. 112(8):2545-50. DOI: 10.1073/pnas.1500643112

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Tanaka, H., Sejnowski, T.J. Motor adaptation and generalization of reaching movements using motor primitives based on spatial coordinates. (2015) Journal of Neurophysiology. 113(4):1217-33. DOI: 10.1152/jn.00002.2014

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Frost, W.N., Brandon, C.J., Bruno, A.M., Humphries, M.D., Moore-Kochlacs, C., Sejnowski, T.J., Wang, J., Hill, E.S. Monitoring Spiking Activity of Many Individual Neurons in Invertebrate Ganglia. (2015) Advances in Experimental Medicine and Biology. 859:127-45. DOI: 10.1007/978-3-319-17641-3_5

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Sejnowski, T.J. Consciousness. (2015) Daedalus. 144(1):123-132. DOI: 10.1162/DAED_a_00321

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Ritaccio, A., Brunner, P., Gunduz, A., Hermes, D., Hirsch, L.J., Jacobs, J., Kamada, K., Kastner, S., Knight, R.T., Lesser, R.P., Miller, K., Sejnowski, T., Worrell, G., Schalk, G. Proceedings of the Fifth International Workshop on Advances in Electrocorticography. (2014) Epilepsy Behav. 41:183-92. DOI: 10.1016/j.yebeh.2014.09.015

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Regner, B.M., Tartakovsky, D.M., Sejnowski, T.J. Identifying transport behavior of single-molecule trajectories. (2014) Biophysical Journal. 107(10):2345-51. DOI: 10.1016/j.bpj.2014.10.005

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Sejnowski, T.J., Churchland, P.S., Movshon, J.A. Putting big data to good use in neuroscience. (2014) Nature Neuroscience. 17(11):1440-1. DOI: 10.1038/nn.3839

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Fields, R.D., Araque, A., Johansen-Berg, H., Lim, S.S., Lynch, G., Nave, K.A., Nedergaard, M., Perez, R., Sejnowski, T., Wake, H. Glial biology in learning and cognition. (2014) The Neuroscientist. 20(5):426-431. DOI: 10.1177/1073858413504465

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Mak-McCully, R.A., Deiss, S.R., Rosen, B.Q., Jung, K.Y., Sejnowski, T.J., Bastuji, H., Rey, M., Cash, S.S., Bazhenov, M., Halgren, E. Synchronization of isolated downstates (K-complexes) may be caused by cortically-induced disruption of thalamic spindling. (2014) PLOS Computational Biology. 10(9):e1003855. DOI: 10.1371/journal.pcbi.1003855

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Sleep spindles and K-complexes (KCs) define stage 2 NREM sleep (N2) in humans. We recently showed that KCs are isolated downstates characterized by widespread cortical silence. We demonstrate here that KCs can be quasi-synchronous across scalp EEG and across much of the cortex using electrocorticography (ECOG) and localized transcortical recordings (bipolar SEEG). We examine the mechanism of synchronous KC production by creating the first conductance based thalamocortical network model of N2 sleep to generate both spontaneous spindles and KCs. Spontaneous KCs are only observed when the model includes diffuse projections from restricted prefrontal areas to the thalamic reticular nucleus (RE), consistent with recent anatomical findings in rhesus monkeys. Modeled KCs begin with a spontaneous focal depolarization of the prefrontal neurons, followed by depolarization of the RE. Surprisingly, the RE depolarization leads to decreased firing due to disrupted spindling, which in turn is due to depolarization-induced inactivation of the low-threshold Ca2+ current (IT). Further, although the RE inhibits thalamocortical (TC) neurons, decreased RE firing causes decreased TC cell firing, again because of disrupted spindling. The resulting abrupt removal of excitatory input to cortical pyramidal neurons then leads to the downstate. Empirically, KCs may also be evoked by sensory stimuli while maintaining sleep. We reproduce this phenomenon in the model by depolarization of either the RE or the widely-projecting prefrontal neurons. Again, disruption of thalamic spindling plays a key role. Higher levels of RE stimulation also cause downstates, but by directly inhibiting the TC neurons. SEEG recordings from the thalamus and cortex in a single patient demonstrated the model prediction that thalamic spindling significantly decreases before KC onset. In conclusion, we show empirically that KCs can be widespread quasi-synchronous cortical downstates, and demonstrate with the first model of stage 2 NREM sleep a possible mechanism whereby this widespread synchrony may arise.

Stefan, M.I., Bartol, T.M., Sejnowski, T.J., Kennedy, M.B. Multi-state modeling of biomolecules. (2014) PLOS Computational Biology. 10(9):e1003844. DOI: 10.1371/journal.pcbi.1003844

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Moldakarimov, S., Bazhenov, M., Sejnowski, T.J. Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning. (2014) PLOS Computational Biology. 10(8):e1003770. DOI: 10.1371/journal.pcbi.1003770

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Lee, H.S., Ghetti, A., Pinto-Duarte, A., Wang, X., Dziewczapolski, G., Galimi, F., Huitron-Resendiz, S., Piña-Crespo, J.C., Roberts, A.J., Verma, I.M., Sejnowski, T.J., Heinemann, S.F. Astrocytes contribute to gamma oscillations and recognition memory. (2014) Proceedings of the National Academy of Sciences of the United States of America. 111(32):E3343-52. DOI: 10.1073/pnas.1410893111

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Weyhenmeyer, J., Hernandez, M.E., Lainscsek, C., Sejnowski, T.J., Poizner, H. Muscle artifacts in single trial EEG data distinguish patients with Parkinson's disease from healthy individuals. (2014) Conf Proc IEEE Eng Med Biol Soc. 2014:3292-3295. DOI: 10.1109/EMBC.2014.6944326

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Broccard, F.D., Mullen, T., Chi, Y.M., Peterson, D., Iversen, J.R., Arnold, M., Kreutz-Delgado, K., Jung, T.P., Makeig, S., Poizner, H., Sejnowski, T., Cauwenberghs, G. Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders. (2014) Ann Biomed Eng. 42(8):1573-1593. DOI: 10.1007/s10439-014-1032-6

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Saremi, S., Sejnowski, T.J. On criticality in high-dimensional data. (2014) Neural Computation. 26(7):1329-1339. DOI: 10.1162/NECO_a_00607

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Solstad, T., Yousif, H.N., Sejnowski, T.J. Place cell rate remapping by CA3 recurrent collaterals. (2014) PLOS Computational Biology. 10(6):e1003648. DOI: 10.1371/journal.pcbi.1003648

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Episodic-like memory is thought to be supported by attractor dynamics in the hippocampus. A possible neural substrate for this memory mechanism is rate remapping, in which the spatial map of place cells encodes contextual information through firing rate variability. To test whether memories are stored as multimodal attractors in populations of place cells, recent experiments morphed one familiar context into another while observing the responses of CA3 cell ensembles. Average population activity in CA3 was reported to transition gradually rather than abruptly from one familiar context to the next, suggesting a lack of attractive forces associated with the two stored representations. On the other hand, individual CA3 cells showed a mix of gradual and abrupt transitions at different points along the morph sequence, and some displayed hysteresis which is a signature of attractor dynamics. To understand whether these seemingly conflicting results are commensurate with attractor network theory, we developed a neural network model of the CA3 with attractors for both position and discrete contexts. We found that for memories stored in overlapping neural ensembles within a single spatial map, position-dependent context attractors made transitions at different points along the morph sequence. Smooth transition curves arose from averaging across the population, while a heterogeneous set of responses was observed on the single unit level. In contrast, orthogonal memories led to abrupt and coherent transitions on both population and single unit levels as experimentally observed when remapping between two independent spatial maps. Strong recurrent feedback entailed a hysteretic effect on the network which diminished with the amount of overlap in the stored memories. These results suggest that context-dependent memory can be supported by overlapping local attractors within a spatial map of CA3 place cells. Similar mechanisms for context-dependent memory may also be found in other regions of the cerebral cortex.

Sejnowski, T.J. Summary: Cognition in 2014. (2014) Cold Spring Harbor Symposia on Quantitative Biology. 79:237-41. DOI: 10.1101/sqb.2014.79.024885

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Sejnowski, T.J., Poizner, H., Lynch, G., Gepshtein, S., Greenspan, R.J. Prospective Optimization. (2014) Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. 102(5). DOI: 10.1109/JPROC.2014.2314297

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Jadi, M.P., Sejnowski, T.J. Cortical oscillations arise from contextual interactions that regulate sparse coding. (2014) Proceedings of the National Academy of Sciences of the United States of America. 111(18):6780-5. DOI: 10.1073/pnas.1405300111

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McDonnell, M.D., Boahen, K., Ijspeert, A., Sejnowski, T.J. Engineering Intelligent Electronic Systems Based on Computational Neuroscience. (2014) Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. 102(5):646-651. DOI: 10.1109/JPROC.2014.2314776

Jadi, M.P., Sejnowski, T.J. Regulating Cortical Oscillations in an Inhibition-Stabilized Network. (2014) Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. 102(5). DOI: 10.1109/JPROC.2014.2313113

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O'Donnell, C., Sejnowski, T.J. Selective memory generalization by spatial patterning of protein synthesis. (2014) Neuron. 82(2):398-412. DOI: 10.1016/j.neuron.2014.02.028

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Edwards, J., Daniel, E., Kinney, J., Bartol, T., Sejnowski, T., Johnston, D., Harris, K., Bajaj, C. VolRoverN: enhancing surface and volumetric reconstruction for realistic dynamical simulation of cellular and subcellular function. (2014) Neuroinformatics. 12(2):277-289. DOI: 10.1007/s12021-013-9205-2

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Yang, L., Leung, H., Peterson, D.A., Sejnowski, T.J., Poizner, H. Toward a semi-self-paced EEG brain computer interface: decoding initiation state from non-initiation state in dedicated time slots. (2014) PLOS One. 9(2):e88915. DOI: 10.1371/journal.pone.0088915

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Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are "synchronous" systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in "asynchronous" BCIs subjects pace the interaction and the system must determine when the subject's control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject's intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs.

Lainscsek, C., Weyhenmeyer, J., Hernandez, M.E., Poizner, H., Sejnowski, T.J. Non-linear dynamical classification of short time series of the rössler system in high noise regimes. (2013) Frontiers in Neurology. 4:182. DOI: 10.3389/fneur.2013.00182

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Time series analysis with delay differential equations (DDEs) reveals non-linear properties of the underlying dynamical system and can serve as a non-linear time-domain classification tool. Here global DDE models were used to analyze short segments of simulated time series from a known dynamical system, the Rössler system, in high noise regimes. In a companion paper, we apply the DDE model developed here to classify short segments of encephalographic (EEG) data recorded from patients with Parkinson's disease and healthy subjects. Nine simulated subjects in each of two distinct classes were generated by varying the bifurcation parameter b and keeping the other two parameters (a and c) of the Rössler system fixed. All choices of b were in the chaotic parameter range. We diluted the simulated data using white noise ranging from 10 to -30 dB signal-to-noise ratios (SNR). Structure selection was supervised by selecting the number of terms, delays, and order of non-linearity of the model DDE model that best linearly separated the two classes of data. The distances d from the linear dividing hyperplane was then used to assess the classification performance by computing the area A' under the ROC curve. The selected model was tested on untrained data using repeated random sub-sampling validation. DDEs were able to accurately distinguish the two dynamical conditions, and moreover, to quantify the changes in the dynamics. There was a significant correlation between the dynamical bifurcation parameter b of the simulated data and the classification parameter d from our analysis. This correlation still held for new simulated subjects with new dynamical parameters selected from each of the two dynamical regimes. Furthermore, the correlation was robust to added noise, being significant even when the noise was greater than the signal. We conclude that DDE models may be used as a generalizable and reliable classification tool for even small segments of noisy data.

Lainscsek, C., Hernandez, M.E., Weyhenmeyer, J., Sejnowski, T.J., Poizner, H. Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson's disease from healthy individuals. (2013) Frontiers in Neurology. 4:200. DOI: 10.3389/fneur.2013.00200

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The pathophysiology of Parkinson's disease (PD) is known to involve altered patterns of neuronal firing and synchronization in cortical-basal ganglia circuits. One window into the nature of the aberrant temporal dynamics in the cerebral cortex of PD patients can come from analysis of the patients electroencephalography (EEG). Rather than using spectral-based methods, we used data models based on delay differential equations (DDE) as non-linear time-domain classification tools to analyze EEG recordings from PD patients on and off dopaminergic therapy and healthy individuals. Two sets of 50 1-s segments of 64-channel EEG activity were recorded from nine PD patients on and off medication and nine age-matched controls. The 64 EEG channels were grouped into 10 clusters covering frontal, central, parietal, and occipital brain regions for analysis. DDE models were fitted to individual trials, and model coefficients and error were used as features for classification. The best models were selected using repeated random sub-sampling validation and classification performance was measured using the area under the ROC curve A'. In a companion paper, we show that DDEs can uncover hidden dynamical structure from short segments of simulated time series of known dynamical systems in high noise regimes. Using the same method for finding the best models, we found here that even short segments of EEG data in PD patients and controls contained dynamical structure, and moreover, that PD patients exhibited a greater dynamic range than controls. DDE model output on the means from one set of 50 trials provided nearly complete separation of PD patients off medication from controls: across brain regions, the area under the receiver-operating characteristic curves, A', varied from 0.95 to 1.0. For distinguishing PD patients on vs. off medication, classification performance A' ranged from 0.86 to 1.0 across brain regions. Moreover, the generalizability of the model to the second set of 50 trials was excellent, with A' ranging from 0.81 to 0.94 across brain regions for controls vs. PD off medication, and from 0.62 to 0.82 for PD on medication vs. off. Finally, model features significantly predicted individual patients' motor severity, as assessed with standard clinical rating scales.

Cortes, J.M., Desroches, M., Rodrigues, S., Veltz, R., Muñoz, M.A., Sejnowski, T.J. Short-term synaptic plasticity in the deterministic Tsodyks-Markram model leads to unpredictable network dynamics. (2013) Proceedings of the National Academy of Sciences of the United States of America. 110(41):16610-5. DOI: 10.1073/pnas.1316071110

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Timofeev, I., Sejnowski, T.J., Bazhenov, M., Chauvette, S., Grand, L.B. Age dependency of trauma-induced neocortical epileptogenesis. (2013) Frontiers in Cellular Neuroscience. 7:154. DOI: 10.3389/fncel.2013.00154

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Lister, R.*, Mukamel, E.A., Nery, J.R., Urich, M., Puddifoot, C.A., Johnson, N.D., Lucero, J., Huang, Y., Dwork, A.J., Schultz, M.D., Yu, M., Tonti-Filippini, J., Heyn, H., Hu, S., Wu, J.C., Rao, A., Esteller, M., He, C., Haghighi, F.G., Sejnowski, T.J., Behrens, M.M.*, Ecker, J.R.* Global epigenomic reconfiguration during mammalian brain development. (2013) Science. 341(6146):1237905. DOI: 10.1126/science.1237905

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Golomb, B.A., Brenner, S., Chalfie, M., Glashow, S.L., Glauber, R.J., Hubel, D.H., Maskin, E.S., Greengard, P., Gross, D.J., Roberts, R., Tonegawa, S., Wilczek, F.A., Brown, E.M., Sejnowski, T.J. Chocolate habits of Nobel prizewinners. (2013) Nature. 499(7459):409. DOI: 10.1038/499409a

Lainscsek, C., Sejnowski, T.J. Electrocardiogram classification using delay differential equations. (2013) Chaos. 23(2):023132. DOI: 10.1063/1.4811544

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Ji, B., Wang, X., Pinto-Duarte, A., Kim, M., Caldwell, S., Young, J.W., Behrens, M.M., Sejnowski, T.J., Geyer, M.A., Zhou, X. Prolonged Ketamine Effects in Sp4 Hypomorphic Mice: Mimicking Phenotypes of Schizophrenia. (2013) PLOS One. 8(6):e66327. DOI: 10.1371/journal.pone.0066327

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Chukoskie, L., Snider, J., Mozer, M.C., Krauzlis, R.J., Sejnowski, T.J. Learning where to look for a hidden target. (2013) Proceedings of the National Academy of Sciences of the United States of America. 110 Suppl :10438-10445. DOI: 10.1073/pnas.1301216110

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Stiefel, K.M., Englitz, B., Sejnowski, T.J. Origin of intrinsic irregular firing in cortical interneurons. (2013) Proceedings of the National Academy of Sciences of the United States of America. 110(19):7886-7891. DOI: 10.1073/pnas.1305219110

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Gire, D.H., Restrepo, D., Sejnowski, T.J., Greer, C., De Carlos, J.A., Lopez-Mascaraque, L. Temporal processing in the olfactory system: can we see a smell? (2013) Neuron. 78(3):416-432. DOI: 10.1016/j.neuron.2013.04.033

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Saremi, S., Sejnowski, T.J., Sharpee, T.O. Double-gabor filters are independent components of small translation-invariant image patches. (2013) Neural Computation. 25(4):922-39. DOI: 10.1162/NECO_a_00418

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Wang, X., Pinto-Duarte, A., Sejnowski, T.J., Behrens, M.M. How Nox2-containing NADPH oxidase affects cortical circuits in the NMDA receptor antagonist model of schizophrenia. (2013) Antioxidants & Redox Signaling. 18(12):1444-62. DOI: 10.1089/ars.2012.4907

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Regner, B.M., Vučinić, D., Domnisoru, C., Bartol, T.M., Hetzer, M.W., Tartakovsky, D.M., Sejnowski, T.J. Anomalous diffusion of single particles in cytoplasm. (2013) Biophysical Journal. 104(8):1652-60. DOI: 10.1016/j.bpj.2013.01.049

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Alivisatos, A.P., Andrews, A.M., Boyden, E.S., Chun, M., Church, G.M., Deisseroth, K., Donoghue, J.P., Fraser, S.E., Lippincott-Schwartz, J., Looger, L.L., Masmanidis, S., McEuen, P.L., Nurmikko, A.V., Park, H., Peterka, D.S., Reid, C., Roukes, M.L., Scherer, A., Schnitzer, M., Sejnowski, T.J., Shepard, K.L., Tsao, D., Turrigiano, G., Weiss, P.S., Xu, C., Yuste, R., Zhuang, X. Nanotools for neuroscience and brain activity mapping. (2013) ACS Nano. 7(3):1850-66. DOI: 10.1021/nn4012847

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Alivisatos, A.P., Chun, M., Church, G.M., Deisseroth, K., Donoghue, J.P., Greenspan, R.J., McEuen, P.L., Roukes, M.L., Sejnowski, T.J., Weiss, P.S., Yuste, R. Neuroscience. The brain activity map. (2013) Science. 339(6125):1284-5. DOI: 10.1126/science.1236939

Saremi, S., Sejnowski, T.J. Hierarchical model of natural images and the origin of scale invariance. (2013) Proceedings of the National Academy of Sciences of the United States of America. 110(8):3071-3076. DOI: 10.1073/pnas.1222618110

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Tanaka, H., Sejnowski, T.J. Computing reaching dynamics in motor cortex with Cartesian spatial coordinates. (2013) Journal of Neurophysiology. 109(4):1182-1201. DOI: 10.1152/jn.00279.2012

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Kinney, J.P., Spacek, J., Bartol, T.M., Bajaj, C.L., Harris, K.M., Sejnowski, T.J. Extracellular sheets and tunnels modulate glutamate diffusion in hippocampal neuropil. (2013) Journal of Comparative Neurology. 521(2):448-464. DOI: 10.1002/cne.23181

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Srinivasan, L., Asaad, W.F., Ginat, D.T., Gale, J.T., Dougherty, D.D., Williams, Z.M., Sejnowski, T.J., Eskandar, E.N. Action initiation in the human dorsal anterior cingulate cortex. (2013) PLOS One. 8(2):e55247. DOI: 10.1371/journal.pone.0055247

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Volman, V., Bazhenov, M., Sejnowski, T.J. Divide and conquer: functional segregation of synaptic inputs by astrocytic microdomains could alleviate paroxysmal activity following brain trauma. (2013) PLOS Computational Biology. 9(1):e1002856. DOI: 10.1371/journal.pcbi.1002856

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Toups, J.V., Fellous, J.M., Thomas, P.J., Sejnowski, T.J., Tiesinga, P.H. Multiple spike time patterns occur at bifurcation points of membrane potential dynamics. (2012) PLOS Computational Biology. 8(10):e1002615. DOI: 10.1371/journal.pcbi.1002615

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Catterall, W.A., Raman, I.M., Robinson, H.P., Sejnowski, T.J., Paulsen, O. The Hodgkin-Huxley heritage: from channels to circuits. (2012) Journal of Neuroscience. 32(41):14064-14073. DOI: 10.1523/JNEUROSCI.3403-12.2012

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Nadkarni, S., Bartol, T.M., Stevens, C.F., Sejnowski, T.J., Levine, H. Short-term plasticity constrains spatial organization of a hippocampal presynaptic terminal. (2012) Proceedings of the National Academy of Sciences of the United States of America. 109(36):14657-14662. DOI: 10.1073/pnas.1211971109

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Volman, V., Bazhenov, M., Sejnowski, T.J. Computational models of neuron-astrocyte interaction in epilepsy. (2012) Frontiers in Computational Neuroscience. 6:58. DOI: 10.3389/fncom.2012.00058

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Bonjean, M., Baker, T., Bazhenov, M., Cash, S., Halgren, E., Sejnowski, T. Interactions between core and matrix thalamocortical projections in human sleep spindle synchronization. (2012) Journal of Neuroscience. 32(15):5250-5263. DOI: 10.1523/JNEUROSCI.6141-11.2012

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Tanaka, H., Krakauer, J.W., Sejnowski, T.J. Generalization and multirate models of motor adaptation. (2012) Neural Computation. 24(4):939-966. DOI: 10.1162/NECO_a_00262

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Ramirez-Moreno, D.F., Sejnowski, T.J. A computational model for the modulation of the prepulse inhibition of the acoustic startle reflex. (2012) Biol Cybern. 106(3):169-176. DOI: 10.1007/s00422-012-0485-7

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Powell, S.B., Sejnowski, T.J., Behrens, M.M. Behavioral and neurochemical consequences of cortical oxidative stress on parvalbumin-interneuron maturation in rodent models of schizophrenia. (2012) Neuropharmacology. 62(3):1322-31. DOI: 10.1016/j.neuropharm.2011.01.049

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Volman, V., Behrens, M.M., Sejnowski, T.J. Downregulation of parvalbumin at cortical GABA synapses reduces network gamma oscillatory activity. (2011) Journal of Neuroscience. 31(49):18137-48. DOI: 10.1523/JNEUROSCI.3041-11.2011

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Coggan, J.S., Ocker, G.K., Sejnowski, T.J., Prescott, S.A. Explaining pathological changes in axonal excitability through dynamical analysis of conductance-based models. (2011) J Neural Eng. 8(6):065002. DOI: 10.1088/1741-2560/8/6/065002

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Wei, H., Bonjean, M., Petry, H.M., Sejnowski, T.J., Bickford, M.E. Thalamic burst firing propensity: a comparison of the dorsal lateral geniculate and pulvinar nuclei in the tree shrew. (2011) Journal of Neuroscience. 31(47):17287-17299. DOI: 10.1523/JNEUROSCI.6431-10.2011

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Brenner, S., Sejnowski, T.J. Understanding the human brain. (2011) Science. 334(6056):567. DOI: 10.1126/science.1215674

Volman, V., Sejnowski, T.J., Bazhenov, M. Topological basis of epileptogenesis in a model of severe cortical trauma. (2011) Journal of Neurophysiology. 106(4):1933-1942. DOI: 10.1152/jn.00458.2011

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Yu, T., Sejnowski, T.J., Cauwenberghs, G. Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI. (2011) IEEE Trans Biomed Circuits Syst. 5(5):420-429. DOI: 10.1109/TBCAS.2011.2169794

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Volman, V., Bazhenov, M., Sejnowski, T.J. Pattern of trauma determines the threshold for epileptic activity in a model of cortical deafferentation. (2011) Proceedings of the National Academy of Sciences of the United States of America. 108(37):15402-15407. DOI: 10.1073/pnas.1112066108

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Dang-Vu, T.T., Bonjean, M., Schabus, M., Boly, M., Darsaud, A., Desseilles, M., Degueldre, C., Balteau, E., Phillips, C., Luxen, A., Sejnowski, T.J., Maquet, P. Interplay between spontaneous and induced brain activity during human non-rapid eye movement sleep. (2011) Proceedings of the National Academy of Sciences of the United States of America. 108(37):15438-15443. DOI: 10.1073/pnas.1112503108

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Toups, J.V., Fellous, J.M., Thomas, P.J., Sejnowski, T.J., Tiesinga, P.H. Finding the event structure of neuronal spike trains. (2011) Neural Computation. 23(9):2169-2208. DOI: 10.1162/NECO_a_00173

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Bonjean, M., Baker, T., Lemieux, M., Timofeev, I., Sejnowski, T., Bazhenov, M. Corticothalamic feedback controls sleep spindle duration in vivo. (2011) Journal of Neuroscience. 31(25):9124-9134. DOI: 10.1523/JNEUROSCI.0077-11.2011

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Romani, S., Sejnowski, T.J., Tsodyks, M. Intracellular dynamics of virtual place cells. (2011) Neural Computation. 23(3):651-5. DOI: 10.1162/NECO_a_00087

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Peterson, D.A., Lotz, D.T., Halgren, E., Sejnowski, T.J., Poizner, H. Choice modulates the neural dynamics of prediction error processing during rewarded learning. (2011) Neuroimage. 54(2):1385-1394. DOI: 10.1016/j.neuroimage.2010.09.051

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Timofeev, I., Bazhenov, M., Seigneur, J., Sejnowski, T. Neocortical synchronization. (2010) Epilepsia. 51(Suppl 5):18. DOI: 10.1111/j.1528-1167.2010.02804.x

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Hill, E.S., Moore-Kochlacs, C., Vasireddi, S.K., Sejnowski, T.J., Frost, W.N. Validation of independent component analysis for rapid spike sorting of optical recording data. (2010) Journal of Neurophysiology. 104(6):3721-3731. DOI: 10.1152/jn.00691.2010

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Sejnowski, T.J. Learning optimal strategies in complex environments. (2010) Proceedings of the National Academy of Sciences of the United States of America. 107(47):20151-2. DOI: 10.1073/pnas.1014954107

Coggan, J.S., Prescott, S.A., Bartol, T.M., Sejnowski, T.J. Imbalance of ionic conductances contributes to diverse symptoms of demyelination. (2010) Proceedings of the National Academy of Sciences of the United States of America. 107(48):20602-20609. DOI: 10.1073/pnas.1013798107

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Tiesinga, P.H., Sejnowski, T.J. Mechanisms for Phase Shifting in Cortical Networks and their Role in Communication through Coherence. (2010) Frontiers in Human Neuroscience. 4:196. DOI: 10.3389/fnhum.2010.00196

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Nadkarni, S., Bartol, T.M., Sejnowski, T.J., Levine, H. Modelling vesicular release at hippocampal synapses. (2010) PLOS Computational Biology. 6(11):e1000983. DOI: 10.1371/journal.pcbi.1000983

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Volman, V., Levine, H., Sejnowski, T.J. Shunting inhibition controls the gain modulation mediated by asynchronous neurotransmitter release in early development. (2010) PLOS Computational Biology. 6(11):e1000973. DOI: 10.1371/journal.pcbi.1000973

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Sejnowski, T.J. QnAs with Terrence J. Sejnowski. [Interviewed by Prashant Nair]. (2010) Proceedings of the National Academy of Sciences of the United States of America. 107(48):20601. DOI: 10.1073/pnas.1015459107

Miller, K.J., Hermes, D., Honey, C.J., Sharma, M., Rao, R.P., den Nijs, M., Fetz, E.E., Sejnowski, T.J., Hebb, A.O., Ojemann, J.G., Makeig, S., Leuthardt, E.C. Dynamic modulation of local population activity by rhythm phase in human occipital cortex during a visual search task. (2010) Frontiers in Human Neuroscience. 4:197. DOI: 10.3389/fnhum.2010.00197

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Hao, J., Lee, T.W., Sejnowski, T.J. Speech Enhancement Using Gaussian Scale Mixture Models. (2010) IEEE Trans Audio Speech Lang Process. 18(6):1127-1136. DOI: 10.1109/TASL.2009.2030012

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Fröhlich, F., Sejnowski, T.J., Bazhenov, M. Network bistability mediates spontaneous transitions between normal and pathological brain states. (2010) Journal of Neuroscience. 30(32):10734-10743. DOI: 10.1523/JNEUROSCI.1239-10.2010

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Hasenstaub, A., Otte, S., Callaway, E., Sejnowski, T.J. Metabolic cost as a unifying principle governing neuronal biophysics. (2010) Proceedings of the National Academy of Sciences of the United States of America. 107(27):12329-34. DOI: 10.1073/pnas.0914886107

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Nelson, J.D., McKenzie, C.R., Cottrell, G.W., Sejnowski, T.J. Experience matters: information acquisition optimizes probability gain. (2010) Psychol Sci. 21(7):960-969. DOI: 10.1177/0956797610372637

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Hao, J., Lee, I., Lee, T.W., Sejnowski, T.J. Independent vector analysis for source separation using a mixture of gaussians prior. (2010) Neural Computation. 22(6):1646-1673. DOI: 10.1162/neco.2010.11-08-906

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Moldakarimov, S., Bazhenov, M., Sejnowski, T.J. Representation sharpening can explain perceptual priming. (2010) Neural Computation. 22(5):1312-32. DOI: 10.1162/neco.2009.04-09-999

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Modchang, C., Nadkarni, S., Bartol, T.M., Triampo, W., Sejnowski, T.J., Levine, H., Rappel, W.J. A comparison of deterministic and stochastic simulations of neuronal vesicle release models. (2010) Phys Biol. 7(2):026008. DOI: 10.1088/1478-3975/7/2/026008

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Wang, H.P., Spencer, D., Fellous, J.M., Sejnowski, T.J. Synchrony of thalamocortical inputs maximizes cortical reliability. (2010) Science. 328(5974):106-109. DOI: 10.1126/science.1183108

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Stiefel, K.M., Fellous, J.M., Thomas, P.J., Sejnowski, T.J. Intrinsic subthreshold oscillations extend the influence of inhibitory synaptic inputs on cortical pyramidal neurons. (2010) European Journal of Neuroscience. 31(6):1019-26. DOI: 10.1111/j.1460-9568.2010.07146.x

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Moldakarimov, S., Bazhenov, M., Sejnowski, T.J. Perceptual priming leads to reduction of gamma frequency oscillations. (2010) Proceedings of the National Academy of Sciences of the United States of America. 107(12):5640-5645. DOI: 10.1073/pnas.0907525107

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Drion, G., Bonjean, M., Waroux, O., Scuvée-Moreau, J., Liégeois, J.F., Sejnowski, T.J., Sepulchre, R., Seutin, V. M-type channels selectively control bursting in rat dopaminergic neurons. (2010) European Journal of Neuroscience. 31(5):827-835. DOI: 10.1111/j.1460-9568.2010.07107.x

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Peterson, D.A., Sejnowski, T.J., Poizner, H. Convergent evidence for abnormal striatal synaptic plasticity in dystonia. (2010) Neurobiol. Dis. 37(3):558-73. DOI: 10.1016/j.nbd.2009.12.003

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Dystonia is a functionally disabling movement disorder characterized by abnormal movements and postures. Although substantial recent progress has been made in identifying genetic factors, the pathophysiology of the disease remains a mystery. A provocative suggestion gaining broader acceptance is that some aspect of neural plasticity may be abnormal. There is also evidence that, at least in some forms of dystonia, sensorimotor "use" may be a contributing factor. Most empirical evidence of abnormal plasticity in dystonia comes from measures of sensorimotor cortical organization and physiology. However, the basal ganglia also play a critical role in sensorimotor function. Furthermore, the basal ganglia are prominently implicated in traditional models of dystonia, are the primary targets of stereotactic neurosurgical interventions, and provide a neural substrate for sensorimotor learning influenced by neuromodulators. Our working hypothesis is that abnormal plasticity in the basal ganglia is a critical link between the etiology and pathophysiology of dystonia. In this review we set up the background for this hypothesis by integrating a large body of disparate indirect evidence that dystonia may involve abnormalities in synaptic plasticity in the striatum. After reviewing evidence implicating the striatum in dystonia, we focus on the influence of two neuromodulatory systems: dopamine and acetylcholine. For both of these neuromodulators, we first describe the evidence for abnormalities in dystonia and then the means by which it may influence striatal synaptic plasticity. Collectively, the evidence suggests that many different forms of dystonia may involve abnormal plasticity in the striatum. An improved understanding of these altered plastic processes would help inform our understanding of the pathophysiology of dystonia, and, given the role of the striatum in sensorimotor learning, provide a principled basis for designing therapies aimed at the dynamic processes linking etiology to pathophysiology of the disease.

Sejnowski, T.J. Consequences of non-uniform active currents in dendrites. (2009) Frontiers in Neuroscience. 3(3):332-333. DOI: 10.3389/neuro.01.038.2009

Volman, V., Levine, H., Ben-Jacob, E., Sejnowski, T.J. Locally balanced dendritic integration by short-term synaptic plasticity and active dendritic conductances. (2009) Journal of Neurophysiology. 102(6):3234-3250. DOI: 10.1152/jn.00260.2009

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Tanaka, H., Sejnowski, T.J., Krakauer, J.W. Adaptation to visuomotor rotation through interaction between posterior parietal and motor cortical areas. (2009) Journal of Neurophysiology. 102(5):2921-32. DOI: 10.1152/jn.90834.2008

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Peterson, D.A., Elliott, C., Song, D.D., Makeig, S., Sejnowski, T.J., Poizner, H. Probabilistic reversal learning is impaired in Parkinson's disease. (2009) Neuroscience. 163(4):1092-1101. DOI: 10.1016/j.neuroscience.2009.07.033

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Behrens, M.M., Sejnowski, T.J. Does schizophrenia arise from oxidative dysregulation of parvalbumin-interneurons in the developing cortex? (2009) Neuropharmacology. 57(3):193-200. DOI: 10.1016/j.neuropharm.2009.06.002

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Tiesinga, P., Sejnowski, T.J. Cortical enlightenment: are attentional gamma oscillations driven by ING or PING? (2009) Neuron. 63(6):727-732. DOI: 10.1016/j.neuron.2009.09.009

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Conner, J.M., Franks, K.M., Titterness, A.K., Russell, K., Merrill, D.A., Christie, B.R., Sejnowski, T.J., Tuszynski, M.H. NGF is essential for hippocampal plasticity and learning. (2009) Journal of Neuroscience. 29(35):10883-10889. DOI: 10.1523/JNEUROSCI.2594-09.2009

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Makeig, S., Gramann, K., Jung, T.P., Sejnowski, T.J., Poizner, H. Linking brain, mind and behavior. (2009) Int J Psychophysiol. 73(2):95-100. DOI: 10.1016/j.ijpsycho.2008.11.008

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Destexhe, A., Sejnowski, T.J. The Wilson-Cowan model, 36 years later. (2009) Biol Cybern. 101(1):1-2. DOI: 10.1007/s00422-009-0328-3

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Meltzoff, A.N., Kuhl, P.K., Movellan, J., Sejnowski, T.J. Foundations for a new science of learning. (2009) Science. 325(5938):284-288. DOI: 10.1126/science.1175626

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Stiefel, K.M., Gutkin, B.S., Sejnowski, T.J. The effects of cholinergic neuromodulation on neuronal phase-response curves of modeled cortical neurons. (2009) Journal of Computational Neuroscience. 26(2):289-301. DOI: 10.1007/s10827-008-0111-9

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Schwartz, O., Sejnowski, T.J., Dayan, P. Perceptual organization in the tilt illusion. (2009) Journal of Vision. 9(4):19.1-20. DOI: 10.1167/9.4.19

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Bazhenov, M., Timofeev, I., Fröhlich, F., Sejnowski, T.J. Cellular and network mechanisms of electrographic seizures. (2009) Drug Discov Today Dis Models. 5(1):45-57. DOI: 10.1016/j.ddmod.2008.07.005

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Hao, J., Attias, H., Nagarajan, S., Lee, T.W., Sejnowski, T.J. Speech Enhancement, Gain, and Noise Spectrum Adaptation Using Approximate Bayesian Estimation. (2009) IEEE Trans Audio Speech Lang Process. 17(1):24-37. DOI: 10.1109/TASL.2008.2005342

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Stiefel, K.M., Gutkin, B.S., Sejnowski, T.J. Cholinergic neuromodulation changes phase response curve shape and type in cortical pyramidal neurons. (2008) PLOS One. 3(12):e3947. DOI: 10.1371/journal.pone.0003947

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Prescott, S.A., Sejnowski, T.J. Spike-rate coding and spike-time coding are affected oppositely by different adaptation mechanisms. (2008) Journal of Neuroscience. 28(50):13649-13661. DOI: 10.1523/JNEUROSCI.1792-08.2008

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Prescott, S.A., Ratté, S., De Koninck, Y., Sejnowski, T.J. Pyramidal neurons switch from integrators in vitro to resonators under in vivo-like conditions. (2008) Journal of Neurophysiology. 100(6):3030-42. DOI: 10.1152/jn.90634.2008

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Fröhlich, F., Bazhenov, M., Iragui-Madoz, V., Sejnowski, T.J. Potassium dynamics in the epileptic cortex: new insights on an old topic. (2008) The Neuroscientist. 14(5):422-433. DOI: 10.1177/1073858408317955

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Kerr, R.A., Bartol, T.M., Kaminsky, B., Dittrich, M., Chang, J.C., Baden, S.B., Sejnowski, T.J., Stiles, J.R. FAST MONTE CARLO SIMULATION METHODS FOR BIOLOGICAL REACTION-DIFFUSION SYSTEMS IN SOLUTION AND ON SURFACES. (2008) SIAM Journal on Scientific Computing. 30(6):3126. DOI: 10.1137/070692017

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Prescott, S.A., De Koninck, Y., Sejnowski, T.J. Biophysical basis for three distinct dynamical mechanisms of action potential initiation. (2008) PLOS Computational Biology. 4(10):e1000198. DOI: 10.1371/journal.pcbi.1000198

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Transduction of graded synaptic input into trains of all-or-none action potentials (spikes) is a crucial step in neural coding. Hodgkin identified three classes of neurons with qualitatively different analog-to-digital transduction properties. Despite widespread use of this classification scheme, a generalizable explanation of its biophysical basis has not been described. We recorded from spinal sensory neurons representing each class and reproduced their transduction properties in a minimal model. With phase plane and bifurcation analysis, each class of excitability was shown to derive from distinct spike initiating dynamics. Excitability could be converted between all three classes by varying single parameters; moreover, several parameters, when varied one at a time, had functionally equivalent effects on excitability. From this, we conclude that the spike-initiating dynamics associated with each of Hodgkin's classes represent different outcomes in a nonlinear competition between oppositely directed, kinetically mismatched currents. Class 1 excitability occurs through a saddle node on invariant circle bifurcation when net current at perithreshold potentials is inward (depolarizing) at steady state. Class 2 excitability occurs through a Hopf bifurcation when, despite net current being outward (hyperpolarizing) at steady state, spike initiation occurs because inward current activates faster than outward current. Class 3 excitability occurs through a quasi-separatrix crossing when fast-activating inward current overpowers slow-activating outward current during a stimulus transient, although slow-activating outward current dominates during constant stimulation. Experiments confirmed that different classes of spinal lamina I neurons express the subthreshold currents predicted by our simulations and, further, that those currents are necessary for the excitability in each cell class. Thus, our results demonstrate that all three classes of excitability arise from a continuum in the direction and magnitude of subthreshold currents. Through detailed analysis of the spike-initiating process, we have explained a fundamental link between biophysical properties and qualitative differences in how neurons encode sensory input.

Lopreore, C.L., Bartol, T.M., Coggan, J.S., Keller, D.X., Sosinsky, G.E., Ellisman, M.H., Sejnowski, T.J. Computational modeling of three-dimensional electrodiffusion in biological systems: application to the node of Ranvier. (2008) Biophysical Journal. 95(6):2624-2635. DOI: 10.1529/biophysj.108.132167

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Low, P.S., Shank, S.S., Sejnowski, T.J., Margoliash, D. Mammalian-like features of sleep structure in zebra finches. (2008) Proceedings of the National Academy of Sciences of the United States of America. 105(26):9081-9086. DOI: 10.1073/pnas.0703452105

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Keller, D.X., Franks, K.M., Bartol, T.M., Sejnowski, T.J. Calmodulin activation by calcium transients in the postsynaptic density of dendritic spines. (2008) PLOS One. 3(4):e2045. DOI: 10.1371/journal.pone.0002045

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Finelli, L.A., Haney, S., Bazhenov, M., Stopfer, M., Sejnowski, T.J. Synaptic learning rules and sparse coding in a model sensory system. (2008) PLOS Computational Biology. 4(4):e1000062. DOI: 10.1371/journal.pcbi.1000062

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Fröhlich, F., Bazhenov, M., Sejnowski, T.J. Pathological effect of homeostatic synaptic scaling on network dynamics in diseases of the cortex. (2008) Journal of Neuroscience. 28(7):1709-1720. DOI: 10.1523/JNEUROSCI.4263-07.2008

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Chen, E., Stiefel, K.M., Sejnowski, T.J., Bullock, T.H. Model of traveling waves in a coral nerve network. (2008) J. Comp. Physiol. A Neuroethol. Sens. Neural. Behav. Physiol. 194(2):195-200. DOI: 10.1007/s00359-007-0305-z

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Tiesinga, P., Fellous, J.M., Sejnowski, T.J. Regulation of spike timing in visual cortical circuits. (2008) Nature Reviews Neuroscience. 9(2):97-107. DOI: 10.1038/nrn2315

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Englitz, B., Stiefel, K.M., Sejnowski, T.J. Irregular firing of isolated cortical interneurons in vitro driven by intrinsic stochastic mechanisms. (2008) Neural Computation. 20(1):44-64. DOI: 10.1162/neco.2008.20.1.44

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Vucinić, D., Sejnowski, T.J. A compact multiphoton 3D imaging system for recording fast neuronal activity. (2007) PLOS One. 2(8):e699. DOI: 10.1371/journal.pone.0000699

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Tour, O., Adams, S.R., Kerr, R.A., Meijer, R.M., Sejnowski, T.J., Tsien, R.W., Tsien, R.Y. Calcium Green FlAsH as a genetically targeted small-molecule calcium indicator. (2007) Nature Chemical Biology. 3(7):423-31. DOI: 10.1038/nchembio.2007.4

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Stiefel, K.M., Sejnowski, T.J. Mapping function onto neuronal morphology. (2007) Journal of Neurophysiology. 98(1):513-26. DOI: 10.1152/jn.00865.2006

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Wachtler, T., Doi, E., Lee, T., Sejnowski, T.J. Cone selectivity derived from the responses of the retinal cone mosaic to natural scenes. (2007) Journal of Vision. 7(8):6. DOI: 10.1167/7.8.6

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Mishra, J., Martinez, A., Sejnowski, T.J., Hillyard, S.A. Early cross-modal interactions in auditory and visual cortex underlie a sound-induced visual illusion. (2007) Journal of Neuroscience. 27(15):4120-4131. DOI: 10.1523/JNEUROSCI.4912-06.2007

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Eagleman, D.M., Sejnowski, T.J. Motion signals bias localization judgments: a unified explanation for the flash-lag, flash-drag, flash-jump, and Frohlich illusions. (2007) Journal of Vision. 7(4):3. DOI: 10.1167/7.4.3

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Delorme, A., Sejnowski, T., Makeig, S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. (2007) Neuroimage. 34(4):1443-1449. DOI: 10.1016/j.neuroimage.2006.11.004

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Siegel, R.M., Duann, J.R., Jung, T.P., Sejnowski, T. Spatiotemporal dynamics of the functional architecture for gain fields in inferior parietal lobule of behaving monkey. (2007) Cerebral Cortex . 17(2):378-390. DOI: 10.1093/cercor/bhj155

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Schwartz, O., Sejnowski, T.J., Dayan, P. Soft mixer assignment in a hierarchical generative model of natural scene statistics. (2006) Neural Computation. 18(11):2680-718. DOI: 10.1162/neco.2006.18.11.2680

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Mishra, J., Fellous, J.M., Sejnowski, T.J. Selective attention through phase relationship of excitatory and inhibitory input synchrony in a model cortical neuron. (2006) Neural Networks. 19(9):1329-1346. DOI: 10.1016/j.neunet.2006.08.005

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Prescott, S.A., Sejnowski, T.J., De Koninck, Y. Reduction of anion reversal potential subverts the inhibitory control of firing rate in spinal lamina I neurons: towards a biophysical basis for neuropathic pain. (2006) Mol Pain. 2:32. DOI: 10.1186/1744-8069-2-32

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Prescott, S.A., Ratté, S., De Koninck, Y., Sejnowski, T.J. Nonlinear interaction between shunting and adaptation controls a switch between integration and coincidence detection in pyramidal neurons. (2006) Journal of Neuroscience. 26(36):9084-97. DOI: 10.1523/JNEUROSCI.1388-06.2006

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Vucinić, D., Bartol, T.M., Sejnowski, T.J. Hybrid reflecting objectives for functional multiphoton microscopy in turbid media. (2006) Opt Lett. 31(16):2447-2449.

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Anemüller, J., Duann, J.R., Sejnowski, T.J., Makeig, S. Spatio-temporal dynamics in fMRI recordings revealed with complex independent component analysis. (2006) Neurocomputing. 69(13-15):1502-1512. DOI: 10.1016/j.neucom.2005.12.029

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Fröhlich, F., Bazhenov, M., Timofeev, I., Steriade, M., Sejnowski, T.J. Slow state transitions of sustained neural oscillations by activity-dependent modulation of intrinsic excitability. (2006) Journal of Neuroscience. 26(23):6153-6162. DOI: 10.1523/JNEUROSCI.5509-05.2006

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Sejnowski, T.J., Paulsen, O. Network oscillations: emerging computational principles. (2006) Journal of Neuroscience. 26(6):1673-6. DOI: 10.1523/JNEUROSCI.3737-05d.2006

Paulsen, O., Sejnowski, T.J. From invertebrate olfaction to human cognition: emerging computational functions of synchronized oscillatory activity. (2006) Journal of Neuroscience. 26(6):1661-1662. DOI: 10.1523/JNEUROSCI.3737-05a.2006

Kerr, R.A., Levine, H., Sejnowski, T.J., Rappel, W.J. Division accuracy in a stochastic model of Min oscillations in Escherichia coli. (2006) Proceedings of the National Academy of Sciences of the United States of America. 103(2):347-352. DOI: 10.1073/pnas.0505825102

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Sample, P.A., Boden, C., Zhang, Z., Pascual, J., Lee, T.W., Zangwill, L.M., Weinreb, R.N., Crowston, J.G., Hoffmann, E.M., Medeiros, F.A., Sejnowski, T., Goldbaum, M. Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields. (2005) Investigative Ophthalmology & Visual Science. 46(10):3684-3692. DOI: 10.1167/iovs.04-1168

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Goldbaum, M.H., Sample, P.A., Zhang, Z., Chan, K., Hao, J., Lee, T.W., Boden, C., Bowd, C., Bourne, R., Zangwill, L., Sejnowski, T., Spinak, D., Weinreb, R.N. Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects. (2005) Investigative Ophthalmology & Visual Science. 46(10):3676-3683. DOI: 10.1167/iovs.04-1167

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Gillette, M.U., Sejnowski, T.J. Physiology. Biological clocks coordinately keep life on time. (2005) Science. 309(5738):1196-1198. DOI: 10.1126/science.1111420

Prank, K., Schulze, E., Eckert, O., Nattkemper, T.W., Bettendorf, M., Maser-Gluth, C., Sejnowski, T.J., Grote, A., Penner, E., von Zur Mühlen, A., Brabant, G. Machine learning approaches for phenotype-genotype mapping: predicting heterozygous mutations in the CYP21B gene from steroid profiles. (2005) Eur. J. Endocrinol. 153(2):301-305. DOI: 10.1530/eje.1.01957

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Coggan, J.S., Bartol, T.M., Esquenazi, E., Stiles, J.R., Lamont, S., Martone, M.E., Berg, D.K., Ellisman, M.H., Sejnowski, T.J. Evidence for ectopic neurotransmission at a neuronal synapse. (2005) Science. 309(5733):446-451. DOI: 10.1126/science.1108239

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Fröhlich, F., Bazhenov, M., Timofeev, I., Sejnowski, T.J. Maintenance and termination of neocortical oscillations by dynamic modulation of intrinsic and synaptic excitability. (2005) Thalamus Relat Syst. 3(2):147-156. DOI: 10.1017/S147292880700015

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Houweling, A.R., Bazhenov, M., Timofeev, I., Steriade, M., Sejnowski, T.J. Homeostatic synaptic plasticity can explain post-traumatic epileptogenesis in chronically isolated neocortex. (2005) Cerebral Cortex . 15(6):834-845. DOI: 10.1093/cercor/bhh184

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Bazhenov, M., Stopfer, M., Sejnowski, T.J., Laurent, G. Fast odor learning improves reliability of odor responses in the locust antennal lobe. (2005) Neuron. 46(3):483-492. DOI: 10.1016/j.neuron.2005.03.022

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Duann, J.R., Jung, T.P., Makeig, S., Sejnowski, T. Repeated decompositions reveal the stability of infomax decomposition of fMRI data. (2005) Conf Proc IEEE Eng Med Biol Soc. 5:5324-5327. DOI: 10.1109/IEMBS.2005.1615683

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Bowd, C., Medeiros, F.A., Zhang, Z., Zangwill, L.M., Hao, J., Lee, T.W., Sejnowski, T.J., Weinreb, R.N., Goldbaum, M.H. Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements. (2005) Investigative Ophthalmology & Visual Science. 46(4):1322-1329. DOI: 10.1167/iovs.04-1122

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Lehky, S.R., Sejnowski, T.J., Desimone, R. Selectivity and sparseness in the responses of striate complex cells. (2005) Vision Research. 45(1):57-73. DOI: 10.1016/j.visres.2004.07.021

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Fuentealba, P., Timofeev, I., Bazhenov, M., Sejnowski, T.J., Steriade, M. Membrane bistability in thalamic reticular neurons during spindle oscillations. (2005) Journal of Neurophysiology. 93(1):294-304. DOI: 10.1152/jn.00552.2004

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Sejnowski, T.J. In memoriam: Francis H.C. Crick. (2004) Neuron. 43(5):619-621. DOI: 10.1016/j.neuron.2004.08.022

Zangwill, L.M., Chan, K., Bowd, C., Hao, J., Lee, T.W., Weinreb, R.N., Sejnowski, T.J., Goldbaum, M.H. Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers. (2004) Investigative Ophthalmology & Visual Science. 45(9):3144-3151. DOI: 10.1167/iovs.04-0202

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Bazhenov, M., Timofeev, I., Steriade, M., Sejnowski, T.J. Potassium model for slow (2-3 Hz) in vivo neocortical paroxysmal oscillations. (2004) Journal of Neurophysiology. 92(2):1116-1132. DOI: 10.1152/jn.00529.2003

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In slow neocortical paroxysmal oscillations, the de- and hyperpolarizing envelopes in neocortical neurons are large compared with slow sleep oscillations. Increased local synchrony of membrane potential oscillations during seizure is reflected in larger electroencephalographic oscillations and the appearance of spike- or polyspike-wave complex recruitment at 2- to 3-Hz frequencies. The oscillatory mechanisms underlying this paroxysmal activity were investigated in computational models of cortical networks. The extracellular K(+) concentration ([K(+)](o)) was continuously computed based on neuronal K(+) currents and K(+) pumps as well as glial buffering. An increase of [K(+)](o) triggered a transition from normal awake-like oscillations to 2- to 3-Hz seizure-like activity. In this mode, the cells fired periodic bursts and nearby neurons oscillated highly synchronously; in some cells depolarization led to spike inactivation lasting 50-100 ms. A [K(+)](o) increase, sufficient to produce oscillations could result from excessive firing (e.g., induced by external stimulation) or inability of K(+) regulatory system (e.g., when glial buffering was blocked). A combination of currents including high-threshold Ca(2+), persistent Na(+) and hyperpolarization-activated depolarizing (I(h)) currents was sufficient to maintain 2- to 3-Hz activity. In a network model that included lateral K(+) diffusion between cells, increase of [K(+)](o) in a small region was generally sufficient to maintain paroxysmal oscillations in the whole network. Slow changes of [K(+)](o) modulated the frequency of bursting and, in some case, led to fast oscillations in the 10- to 15-Hz frequency range, similar to the fast runs observed during seizures in vivo. These results suggest that modifications of the intrinsic currents mediated by increase of [K(+)](o) can explain the range of neocortical paroxysmal oscillations in vivo.

Sample, P.A., Chan, K., Boden, C., Lee, T.W., Blumenthal, E.Z., Weinreb, R.N., Bernd, A., Pascual, J., Hao, J., Sejnowski, T., Goldbaum, M.H. Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects. (2004) Investigative Ophthalmology & Visual Science. 45(8):2596-2605. DOI: 10.1167/iovs.03-0343

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Shon, A.P., Rao, R.P., Sejnowski, T.J. Motion detection and prediction through spike-timing dependent plasticity. (2004) Network. 15(3):179-198.

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Tiesinga, P.H., Fellous, J.M., Salinas, E., José, J.V., Sejnowski, T.J. Inhibitory synchrony as a mechanism for attentional gain modulation. (2004) Journal of Physiology, Paris. 98(4-6):296-314. DOI: 10.1016/j.jphysparis.2005.09.002

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Recordings from area V4 of monkeys have revealed that when the focus of attention is on a visual stimulus within the receptive field of a cortical neuron, two distinct changes can occur: The firing rate of the neuron can change and there can be an increase in the coherence between spikes and the local field potential (LFP) in the gamma-frequency range (30-50 Hz). The hypothesis explored here is that these observed effects of attention could be a consequence of changes in the synchrony of local interneuron networks. We performed computer simulations of a Hodgkin-Huxley type neuron driven by a constant depolarizing current, I, representing visual stimulation and a modulatory inhibitory input representing the effects of attention via local interneuron networks. We observed that the neuron's firing rate and the coherence of its output spike train with the synaptic inputs was modulated by the degree of synchrony of the inhibitory inputs. When inhibitory synchrony increased, the coherence of spiking model neurons with the synaptic input increased, but the firing rate either increased or remained the same. The mean number of synchronous inhibitory inputs was a key determinant of the shape of the firing rate versus current (f-I) curves. For a large number of inhibitory inputs (approximately 50), the f-I curve saturated for large I and an increase in input synchrony resulted in a shift of sensitivity-the model neuron responded to weaker inputs I. For a small number (approximately 10), the f-I curves were non-saturating and an increase in input synchrony led to an increase in the gain of the response-the firing rate in response to the same input was multiplied by an approximately constant factor. The firing rate modulation with inhibitory synchrony was highest when the input network oscillated in the gamma frequency range. Thus, the observed changes in firing rate and coherence of neurons in the visual cortex could be controlled by top-down inputs that regulated the coherence in the activity of a local inhibitory network discharging at gamma frequencies.

Fuentealba, P., Crochet, S., Timofeev, I., Bazhenov, M., Sejnowski, T.J., Steriade, M. Experimental evidence and modeling studies support a synchronizing role for electrical coupling in the cat thalamic reticular neurons in vivo. (2004) European Journal of Neuroscience. 20(1):111-9. DOI: 10.1111/j.1460-9568.2004.03462.x

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Bowd, C., Zangwill, L.M., Medeiros, F.A., Hao, J., Chan, K., Lee, T.W., Sejnowski, T.J., Goldbaum, M.H., Sample, P.A., Crowston, J.G., Weinreb, R.N. Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucoma-suspect eyes. (2004) Investigative Ophthalmology & Visual Science. 45(7):2255-2262.

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Schwartz, O., Movellan, J.R., Wachtler, T., Albright, T.D., Sejnowski, T.J. Spike count distributions, factorizability, and contextual effects in area V1. (2004) Neurocomputing. 58-60:893-900. DOI: 10.1016/j.neucom.2004.01.143

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Arnold, M., Sejnowski, T., Hammerstrom, D., Jabri, M. Neural systems integration. (2004) Neurocomputing. 58-60:1123-1128. DOI: 10.1016/j.neucom.2004.01.176

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Makeig, S., Delorme, A., Westerfield, M., Jung, T.P., Townsend, J., Courchesne, E., Sejnowski, T.J. Electroencephalographic brain dynamics following manually responded visual targets. (2004) PLOS Biology. 2(6):e176. DOI: 10.1371/journal.pbio.0020176

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Tiesinga, P.H., Fellous, J.M., Salinas, E., José, J.V., Sejnowski, T.J. Synchronization as a mechanism for attentional gain modulation. (2004) Neurocomputing. 58-60:641-646. DOI: 10.1016/j.neucom.2004.01.108

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Yates, P.A., Holub, A.D., McLaughlin, T., Sejnowski, T.J., O'Leary, D.D. Computational modeling of retinotopic map development to define contributions of EphA-ephrinA gradients, axon-axon interactions, and patterned activity. (2004) Journal of Neurobiology. 59(1):95-113. DOI: 10.1002/neu.10341

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The topographic projection of retinal ganglion cell (RGC) axons to mouse superior colliculus (SC) or chick optic tectum (OT) is formed in three phases: RGC axons overshoot their termination zone (TZ); they exhibit interstitial branching along the axon that is topographically biased for the correct location of their future TZ; and branches arborize preferentially at the TZ and the initial exuberant projection refines through axon and branch elimination to generate a precise retinotopic map. We present a computational model of map development that demonstrates that the countergradients of EphAs and ephrinAs in retina and the OT/SC and bidirectional repellent signaling between RGC axons and OT/SC cells are sufficient to direct an initial topographic bias in RGC axon branching. Our model also suggests that a proposed repellent action of EphAs/ephrinAs present on RGC branches and arbors added to that of EphAs/ephrinAs expressed by OT/SC cells is required to progressively restrict branching and arborization to topographically correct locations and eliminate axon overshoot. Simulations show that this molecular framework alone can develop considerable topographic order and refinement, including axon elimination, a feature not programmed into the model. Generating a refined map with a condensed TZ as in vivo requires an additional parameter that enhances branch formation along an RGC axon near sites that it has a higher branch density, and resembles an assumed role for patterned neural activity. The same computational model generates the phenotypes reported in ephrinA deficient mice and Isl2-EphA3 knockin mice. This modeling suggests that gradients of counter-repellents can establish a substantial degree of topographic order in the OT/SC, and that repellents present on RGC axon branches and arbors make a substantial contribution to map refinement. However, competitive interactions between RGC axons that enhance the probability of continued local branching are required to generate precise retinotopy.

Eagleman, D.M., Jacobson, J.E., Sejnowski, T.J. Perceived luminance depends on temporal context. (2004) Nature. 428(6985):854-856. DOI: 10.1038/nature02467

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Fellous, J.M., Tiesinga, P.H., Thomas, P.J., Sejnowski, T.J. Discovering spike patterns in neuronal responses. (2004) Journal of Neuroscience. 24(12):2989-3001. DOI: 10.1523/JNEUROSCI.4649-03.2004

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Tiesinga, P.H., Sejnowski, T.J. Rapid temporal modulation of synchrony by competition in cortical interneuron networks. (2004) Neural Computation. 16(2):251-75. DOI: 10.1162/089976604322742029

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Schreiber, S., Fellous, J.M., Tiesinga, P., Sejnowski, T.J. Influence of ionic conductances on spike timing reliability of cortical neurons for suprathreshold rhythmic inputs. (2004) Journal of Neurophysiology. 91(1):194-205. DOI: 10.1152/jn.00556.2003

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Fellous, J.M., Sejnowski, T.J. Regulation of persistent activity by background inhibition in an in vitro model of a cortical microcircuit. (2003) Cerebral Cortex . 13(11):1232-1241.

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Anemüller, J., Sejnowski, T.J., Makeig, S. Complex independent component analysis of frequency-domain electroencephalographic data. (2003) Neural Networks. 16(9):1311-1323. DOI: 10.1016/j.neunet.2003.08.003

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Sejnowski, T. The computational self. (2003) Ann. N. Y. Acad. Sci. 1001:262-271.

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Destexhe, A., Sejnowski, T.J. Interactions between membrane conductances underlying thalamocortical slow-wave oscillations. (2003) Physiological Reviews. 83(4):1401-1453. DOI: 10.1152/physrev.00012.2003

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Laughlin, S.B., Sejnowski, T.J. Communication in neuronal networks. (2003) Science. 301(5641):1870-1874. DOI: 10.1126/science.1089662

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Eagleman, D.M., Sejnowski, T.J. The line-motion illusion can be reversed by motion signals after the line disappears. (2003) Perception. 32(8):963-968. DOI: 10.1068/p3314a

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Sejnowski, T.J. Perceptions of science. Tap into science 24-7. (2003) Science. 301(5633):601. DOI: 10.1126/science.1084463

Kretzberg, J., Sejnowski, T., Warzecha, A.K., Egelhaaf, M. Variability of postsynaptic responses depends non-linearly on the number of synaptic inputs. (2003) Neurocomputing. 52-54:313-320. DOI: 10.1016/S0925-2312(02)00797-X

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Schreiber, S., Fellous, J.M., Whitmer, D., Tiesinga, P., Sejnowski, T.J. A new correlation-based measure of spike timing reliability. (2003) Neurocomputing. 52-54:925-931. DOI: 10.1016/S0925-2312(02)00838-X

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Rao, R.P., Sejnowski, T.J. Self-organizing neural systems based on predictive learning. (2003) Philos Trans A Math Phys Eng Sci. 361(1807):1149-1175. DOI: 10.1098/rsta.2003.1190

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Thomas, P.J., Tiesinga, P.H., Fellous, J.M., Sejnowski, T.J. Reliability and bifurcation in neurons driven by multiple sinusoids. (2003) Neurocomputing. 52-54:955-961. DOI: 10.1016/S0925-2312(02)00856-1

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Neubig, M., Destexhe, A., Sejnowski, T.J. Variability of quantal synaptic currents in thalamocortical neurons. (2003) Thalamus Relat Syst. 2(2):153-168. DOI: 10.1016/S1472-9288(03)00008-6

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Franks, K.M., Stevens, C.F., Sejnowski, T.J. Independent sources of quantal variability at single glutamatergic synapses. (2003) Journal of Neuroscience. 23(8):3186-3195.

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Fellous, J.M., Rudolph, M., Destexhe, A., Sejnowski, T.J. Synaptic background noise controls the input/output characteristics of single cells in an in vitro model of in vivo activity. (2003) Neuroscience. 122(3):811-829.

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Wachtler, T., Sejnowski, T.J., Albright, T.D. Representation of color stimuli in awake macaque primary visual cortex. (2003) Neuron. 37(4):681-691.

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Doi, E., Inui, T., Lee, T.W., Wachtler, T., Sejnowski, T.J. Spatiochromatic receptive field properties derived from information-theoretic analyses of cone mosaic responses to natural scenes. (2003) Neural Computation. 15(2):397-417. DOI: 10.1162/089976603762552960

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Kreutz-Delgado, K., Murray, J.F., Rao, B.D., Engan, K., Lee, T.W., Sejnowski, T.J. Dictionary learning algorithms for sparse representation. (2003) Neural Computation. 15(2):349-96. DOI: 10.1162/089976603762552951

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Rao, R.P., Sejnowski, T.J. Complex Cell-like Direction Selectivity through Spike-Timing Dependent Plasticity. (2003) IETE Journal of Research. 49(2-3):97-111.

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Suri, R.E., Sejnowski, T.J. Spike propagation synchronized by temporally asymmetric Hebbian learning. (2002) Biol Cybern. 87(5-6):440-5. DOI: 10.1007/s00422-002-0355-9

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Franks, K.M., Sejnowski, T.J. Complexity of calcium signaling in synaptic spines. (2002) BioEssays. 24(12):1130-1144. DOI: 10.1002/bies.10193

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Destexhe, A., Sejnowski, T.J. The initiation of bursts in thalamic neurons and the cortical control of thalamic sensitivity. (2002) Philos. Trans. R. Soc. Lond., B, Biol. Sci. 357(1428):1649-57. DOI: 10.1098/rstb.2002.1154

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Bartlett, M.S., Movellan, J.R., Sejnowski, T.J. Face recognition by independent component analysis. (2002) IEEE Trans Neural Netw. 13(6):1450-1464. DOI: 10.1109/TNN.2002.804287

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Franks, K.M., Bartol, T.M., Sejnowski, T.J. A Monte Carlo model reveals independent signaling at central glutamatergic synapses. (2002) Biophysical Journal. 83(5):2333-2348. DOI: 10.1016/S0006-3495(02)75248-X

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Bowd, C., Chan, K., Zangwill, L.M., Goldbaum, M.H., Lee, T.W., Sejnowski, T.J., Weinreb, R.N. Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc. (2002) Investigative Ophthalmology & Visual Science. 43(11):3444-3454.

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Bazhenov, M., Timofeev, I., Steriade, M., Sejnowski, T.J. Model of thalamocortical slow-wave sleep oscillations and transitions to activated States. (2002) Journal of Neuroscience. 22(19):8691-704.

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Chan, K., Lee, T.W., Sample, P.A., Goldbaum, M.H., Weinreb, R.N., Sejnowski, T.J. Comparison of machine learning and traditional classifiers in glaucoma diagnosis. (2002) IEEE Trans Biomed Eng. 49(9):963-74. DOI: 10.1109/TBME.2002.802012

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Salinas, E., Sejnowski, T.J. Integrate-and-fire neurons driven by correlated stochastic input. (2002) Neural Computation. 14(9):2111-2155. DOI: 10.1162/089976602320264024

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Chan, K., Lee, T.W., Sejnowski, T.J. Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components. (2002) J Mach Learn Res. 3:99-114.

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Lee, T.W., Wachtler, T., Sejnowski, T.J. Color opponency is an efficient representation of spectral properties in natural scenes. (2002) Vision Research. 42(17):2095-2103.

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Sample, P.A., Goldbaum, M.H., Chan, K., Boden, C., Lee, T.W., Vasile, C., Boehm, A.G., Sejnowski, T., Johnson, C.A., Weinreb, R.N. Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields. (2002) Investigative Ophthalmology & Visual Science. 43(8):2660-2665.

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Houweling, A.R., Bazhenov, M., Timofeev, I., Grenier, F., Steriade, M., Sejnowski, T.J. Frequency-selective augmenting responses by short-term synaptic depression in cat neocortex. (2002) Journal of Physiology. 542(Pt 2):599-617.

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Tiesinga, P.H., Fellous, J.M., Sejnowski, T.J. Attractor reliability reveals deterministic structure in neuronal spike trains. (2002) Neural Computation. 14(7):1629-1650. DOI: 10.1162/08997660260028647

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Timofeev, I., Grenier, F., Bazhenov, M., Houweling, A.R., Sejnowski, T.J., Steriade, M. Short- and medium-term plasticity associated with augmenting responses in cortical slabs and spindles in intact cortex of cats in vivo. (2002) Journal of Physiology. 542(Pt 2):583-598.

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Plastic changes in the synaptic responsiveness of neocortical neurones, which occur after rhythmic stimuli within the frequency range of sleep spindles (10 Hz), were investigated in isolated neocortical slabs and intact cortex of anaesthetized cats by means of single, dual and triple simultaneous intracellular recordings in conjunction with recordings of local field potential responses. In isolated cortical slabs (10 mm long, 6 mm wide and 4-5 mm deep), augmenting responses to pulse-trains at 10 Hz (responses with growing amplitudes from the second stimulus in a train) were elicited only by relatively high-intensity stimuli. At low intensities, responses were decremental. The largest augmenting responses were evoked in neurones located close to the stimulation site. Quantitative analyses of the number of action potentials and the amplitude and area of depolarization during augmenting responses in a population of neurones recorded from slabs showed that the most dramatic increases in the number of spikes with successive stimuli, and the greatest increase in depolarization amplitude, were found in conventional fast-spiking (FS) neurones. The largest increase in the area of depolarization was found in regular-spiking (RS) neurones. Dual intracellular recordings from a pair of FS and RS neurones in the slab revealed more action potentials in the FS neurone during augmenting responses and a significant increase in the depolarization area of the RS neurone that was dependent on the firing of the FS neurone. Self-sustained seizures could occur in the slab after rhythmic stimuli at 10 Hz. In the intact cortex, repeated sequences of stimuli generating augmenting responses or spontaneous spindles could induce an increased synaptic responsiveness to single stimuli, which lasted for several minutes. A similar time course of increased responsiveness was obtained with induction of cellular plasticity. These data suggest that augmenting responses elicited by stimulation, as well as spontaneously occurring spindles, may induce short- and medium-term plasticity of neuronal responses.

Timofeev, I., Bazhenov, M., Sejnowski, T., Steriade, M. Cortical hyperpolarization-activated depolarizing current takes part in the generation of focal paroxysmal activities. (2002) Proceedings of the National Academy of Sciences of the United States of America. 99(14):9533-9537. DOI: 10.1073/pnas.132259899

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Eagleman, D.M., Sejnowski, T.J. Untangling spatial from temporal illusions. (2002) Trends in Neurosciences. 25(6):293; author reply 294.

Wiskott, L., Sejnowski, T.J. Slow feature analysis: unsupervised learning of invariances. (2002) Neural Computation. 14(4):715-770. DOI: 10.1162/089976602317318938

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Duann, J.R., Jung, T.P., Kuo, W.J., Yeh, T.C., Makeig, S., Hsieh, J.C., Sejnowski, T.J. Single-trial variability in event-related BOLD signals. (2002) Neuroimage. 15(4):823-835. DOI: 10.1006/nimg.2001.1049

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Tiesinga, P.H., Fellous, J.M., José, J.V., Sejnowski, T.J. Information transfer in entrained cortical neurons. (2002) Network. 13(1):41-66.

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Makeig, S., Westerfield, M., Jung, T.P., Enghoff, S., Townsend, J., Courchesne, E., Sejnowski, T.J. Dynamic brain sources of visual evoked responses. (2002) Science. 295(5555):690-4. DOI: 10.1126/science.1066168

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Goldbaum, M.H., Sample, P.A., Chan, K., Williams, J., Lee, T.W., Blumenthal, E., Girkin, C.A., Zangwill, L.M., Bowd, C., Sejnowski, T., Weinreb, R.N. Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. (2002) Investigative Ophthalmology & Visual Science. 43(1):162-9.

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Flash, T., Sejnowski, T.J. Computational approaches to motor control. (2001) Current Opinion in Neurobiology. 11(6):655-62.

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Destexhe, A., Rudolph, M., Fellous, J.M., Sejnowski, T.J. Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. (2001) Neuroscience. 107(1):13-24.

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Jung, T.P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., Sejnowski, T.J. Analysis and visualization of single-trial event-related potentials. (2001) Hum Brain Mapp. 14(3):166-85.

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In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data.

Salinas, E., Sejnowski, T.J. Gain modulation in the central nervous system: where behavior, neurophysiology, and computation meet. (2001) The Neuroscientist. 7(5):430-40. DOI: 10.1177/107385840100700512

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Rao, R.P., Sejnowski, T.J. Predictive learning of temporal sequences in recurrent neocortical circuits. (2001) Novartis Found. Symp. 239:208-229; discussion 229-240.

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Salinas, E., Sejnowski, T.J. Correlated neuronal activity and the flow of neural information. (2001) Nature Reviews Neuroscience. 2(8):539-50. DOI: 10.1038/35086012

Jung, T.P., Makeig, S., McKeown, M.J., Bell, A.J., Lee, T.W., Sejnowski, T.J. Imaging Brain Dynamics Using Independent Component Analysis. (2001) Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. 89(7):1107-1122. DOI: 10.1109/5.939827

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Pouget, A., Sejnowski, T.J. Simulating a lesion in a basis function model of spatial representations: comparison with hemineglect. (2001) Psychol Rev. 108(3):653-73.

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Läer, L., Kloppstech, M., Schöfl, C., Sejnowski, T.J., Brabant, G., Prank, K. Noise enhanced hormonal signal transduction through intracellular calcium oscillations. (2001) Biophysical Chemistry. 91(2):157-66.

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Tiesinga, P.H., Fellous, J.M., José, J.V., Sejnowski, T.J. Computational model of carbachol-induced delta, theta, and gamma oscillations in the hippocampus. (2001) Hippocampus. 11(3):251-274. DOI: 10.1002/hipo.1041

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Zhang, K., Sejnowski, T.J. Accuracy and learning in neuronal populations. (2001) Progress in Brain Research. 130:333-342.

Rao, R.P., Eagleman, D.M., Sejnowski, T.J. Optimal smoothing in visual motion perception. (2001) Neural Computation. 13(6):1243-53.

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Tiesinga, P.H., Sejnowski, T.J. Precision of pulse-coupled networks of integrate-and-fire neurons. (2001) Network. 12(2):215-33.

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Bazhenov, M., Stopfer, M., Rabinovich, M., Abarbanel, H.D., Sejnowski, T.J., Laurent, G. Model of cellular and network mechanisms for odor-evoked temporal patterning in the locust antennal lobe. (2001) Neuron. 30(2):569-81.

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Bazhenov, M., Stopfer, M., Rabinovich, M., Huerta, R., Abarbanel, H.D., Sejnowski, T.J., Laurent, G. Model of transient oscillatory synchronization in the locust antennal lobe. (2001) Neuron. 30(2):553-567.

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Wachtler, T., Albright, T.D., Sejnowski, T.J. Nonlocal interactions in color perception: nonlinear processing of chromatic signals from remote inducers. (2001) Vision Research. 41(12):1535-1546.

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Fellous, J.M., Houweling, A.R., Modi, R.H., Rao, R.P., Tiesinga, P.H., Sejnowski, T.J. Frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons. (2001) Journal of Neurophysiology. 85(4):1782-1787.

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Vissel, B., Royle, G.A., Christie, B.R., Schiffer, H.H., Ghetti, A., Tritto, T., Perez-Otano, I., Radcliffe, R.A., Seamans, J., Sejnowski, T., Wehner, J.M., Collins, A.C., O'Gorman, S., Heinemann, S.F. The role of RNA editing of kainate receptors in synaptic plasticity and seizures. (2001) Neuron. 29(1):217-227.

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Brown, G.D., Yamada, S., Sejnowski, T.J. Independent component analysis at the neural cocktail party. (2001) Trends in Neurosciences. 24(1):54-63.

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Wachtler, T., Lee, T.W., Sejnowski, T.J. Chromatic structure of natural scenes. (2001) J Opt Soc Am A Opt Image Sci Vis. 18(1):65-77.

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Seamans, J.K., Durstewitz, D., Christie, B.R., Stevens, C.F., Sejnowski, T.J. Dopamine D1/D5 receptor modulation of excitatory synaptic inputs to layer V prefrontal cortex neurons. (2001) Proceedings of the National Academy of Sciences of the United States of America. 98(1):301-306. DOI: 10.1073/pnas.011518798

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Tiesinga, P.H., José, J.V., Sejnowski, T.J. Comparison of current-driven and conductance-driven neocortical model neurons with Hodgkin-Huxley voltage-gated channels. (2000) Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 62(6 Pt B):8413-8419.

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Sejnowski, T.J., Destexhe, A. Why do we sleep? (2000) Brain Research. 886(1-2):208-223.

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Makeig, S., Jung, T.P., Sejnowski, T.J. Awareness during drowsiness: dynamics and electrophysiological correlates. (2000) Can J Exp Psychol. 54(4):266-273.

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Christie, B.R., Franks, K.M., Seamans, J.K., Saga, K., Sejnowski, T.J. Synaptic plasticity in morphologically identified CA1 stratum radiatum interneurons and giant projection cells. (2000) Hippocampus. 10(6):673-683. DOI: 10.1002/1098-1063(2000)10:6<673::AID-HIPO1005>3.0.

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Timofeev, I., Grenier, F., Bazhenov, M., Sejnowski, T.J., Steriade, M. Origin of slow cortical oscillations in deafferented cortical slabs. (2000) Cerebral Cortex . 10(12):1185-1199.

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Durstewitz, D., Seamans, J.K., Sejnowski, T.J. Neurocomputational models of working memory. (2000) Nature Neuroscience. 3 Suppl:1184-91. DOI: 10.1038/81460

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Jung, T.P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., Sejnowski, T.J. Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. (2000) Clin Neurophysiol. 111(10):1745-58.

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Bazhenov, M., Timofeev, I., Steriade, M., Sejnowski, T. Spiking-bursting activity in the thalamic reticular nucleus initiates sequences of spindle oscillations in thalamic networks. (2000) Journal of Neurophysiology. 84(2):1076-87.

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Salinas, E., Sejnowski, T.J. Impact of correlated synaptic input on output firing rate and variability in simple neuronal models. (2000) Journal of Neuroscience. 20(16):6193-209.

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Krekelberg, B., Lappe, M., Whitney, D., Cavanagh, P., Eagleman, D.M., Sejnowski, T.J. The position of moving objects. (2000) Science. 289(5482):1107a. DOI: 10.1126/science.289.5482.1107a

Makeig, S., Enghoff, S., Jung, T.P., Sejnowski, T.J. A natural basis for efficient brain-actuated control. (2000) IEEE Trans Rehabil Eng. 8(2):208-11.

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Zhang, K., Sejnowski, T.J. A universal scaling law between gray matter and white matter of cerebral cortex. (2000) Proceedings of the National Academy of Sciences of the United States of America. 97(10):5621-6. DOI: 10.1073/pnas.090504197

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Paulsen, O., Sejnowski, T.J. Natural patterns of activity and long-term synaptic plasticity. (2000) Current Opinion in Neurobiology. 10(2):172-9.

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Fellous, J.M., Sejnowski, T.J. Cholinergic induction of oscillations in the hippocampal slice in the slow (0.5-2 Hz), theta (5-12 Hz), and gamma (35-70 Hz) bands. (2000) Hippocampus. 10(2):187-97. DOI: 10.1002/(SICI)1098-1063(2000)10:2<187::AID-HIPO8>3

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Jung, T.P., Makeig, S., Humphries, C., Lee, T.W., McKeown, M.J., Iragui, V., Sejnowski, T.J. Removing electroencephalographic artifacts by blind source separation. (2000) Psychophysiology. 37(2):163-78.

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Eagleman, D.M., Sejnowski, T.J. Motion integration and postdiction in visual awareness. (2000) Science. 287(5460):2036-8.

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Durstewitz, D., Seamans, J.K., Sejnowski, T.J. Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex. (2000) Journal of Neurophysiology. 83(3):1733-50.

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The prefrontal cortex (PFC) is critically involved in working memory, which underlies memory-guided, goal-directed behavior. During working-memory tasks, PFC neurons exhibit sustained elevated activity, which may reflect the active holding of goal-related information or the preparation of forthcoming actions. Dopamine via the D1 receptor strongly modulates both this sustained (delay-period) activity and behavioral performance in working-memory tasks. However, the function of dopamine during delay-period activity and the underlying neural mechanisms are only poorly understood. Recently we proposed that dopamine might stabilize active neural representations in PFC circuits during tasks involving working memory and render them robust against interfering stimuli and noise. To further test this idea and to examine the dopamine-modulated ionic currents that could give rise to increased stability of neural representations, we developed a network model of the PFC consisting of multicompartment neurons equipped with Hodgkin-Huxley-like channel kinetics that could reproduce in vitro whole cell and in vivo recordings from PFC neurons. Dopaminergic effects on intrinsic ionic and synaptic conductances were implemented in the model based on in vitro data. Simulated dopamine strongly enhanced high, delay-type activity but not low, spontaneous activity in the model network. Furthermore the strength of an afferent stimulation needed to disrupt delay-type activity increased with the magnitude of the dopamine-induced shifts in network parameters, making the currently active representation much more stable. Stability could be increased by dopamine-induced enhancements of the persistent Na(+) and N-methyl-D-aspartate (NMDA) conductances. Stability also was enhanced by a reduction in AMPA conductances. The increase in GABA(A) conductances that occurs after stimulation of dopaminergic D1 receptors was necessary in this context to prevent uncontrolled, spontaneous switches into high-activity states (i.e., spontaneous activation of task-irrelevant representations). In conclusion, the dopamine-induced changes in the biophysical properties of intrinsic ionic and synaptic conductances conjointly acted to highly increase stability of activated representations in PFC networks and at the same time retain control over network behavior and thus preserve its ability to adequately respond to task-related stimuli. Predictions of the model can be tested in vivo by locally applying specific D1 receptor, NMDA, or GABA(A) antagonists while recording from PFC neurons in delayed reaction-type tasks with interfering stimuli.

Moortgat, K.T., Bullock, T.H., Sejnowski, T.J. Gap junction effects on precision and frequency of a model pacemaker network. (2000) Journal of Neurophysiology. 83(2):984-97.

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Moortgat, K.T., Bullock, T.H., Sejnowski, T.J. Precision of the pacemaker nucleus in a weakly electric fish: network versus cellular influences. (2000) Journal of Neurophysiology. 83(2):971-83.

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We investigated the relative influence of cellular and network properties on the extreme spike timing precision observed in the medullary pacemaker nucleus (Pn) of the weakly electric fish Apteronotus leptorhynchus. Of all known biological rhythms, the electric organ discharge of this and related species is the most temporally precise, with a coefficient of variation (CV = standard deviation/mean period) of 2 x 10(-4) and standard deviation (SD) of 0.12-1.0 micros. The timing of the electric organ discharge is commanded by neurons of the Pn, individual cells of which we show in an in vitro preparation to have only a slightly lesser degree of precision. Among the 100-150 Pn neurons, dye injection into a pacemaker cell resulted in dye coupling in one to five other pacemaker cells and one to three relay cells, consistent with previous results. Relay cell fills, however, showed profuse dendrites and contacts never seen before: relay cell dendrites dye-coupled to one to seven pacemaker and one to seven relay cells. Moderate (0.1-10 nA) intracellular current injection had no effect on a neuron's spiking period, and only slightly modulated its spike amplitude, but could reset the spike phase. In contrast, massive hyperpolarizing current injections (15-25 nA) could force the cell to skip spikes. The relative timing of subthreshold and full spikes suggested that at least some pacemaker cells are likely to be intrinsic oscillators. The relative amplitudes of the subthreshold and full spikes gave a lower bound to the gap junctional coupling coefficient of 0.01-0.08. Three drugs, called gap junction blockers for their mode of action in other preparations, caused immediate and substantial reduction in frequency, altered the phase lag between pairs of neurons, and later caused the spike amplitude to drop, without altering the spike timing precision. Thus we conclude that the high precision of the normal Pn rhythm does not require maximal gap junction conductances between neurons that have ordinary cellular precision. Rather, the spiking precision can be explained as an intrinsic cellular property while the gap junctions act to frequency- and phase-lock the network oscillations.

Lewicki, M.S., Sejnowski, T.J. Learning overcomplete representations. (2000) Neural Computation. 12(2):337-65.

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Murthy, V.N., Sejnowski, T.J., Stevens, C.F. Dynamics of dendritic calcium transients evoked by quantal release at excitatory hippocampal synapses. (2000) Proceedings of the National Academy of Sciences of the United States of America. 97(2):901-6.

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Sejnowski, T.J. The book of Hebb. (1999) Neuron. 24(4):773-6.

van Praag, H., Christie, B.R., Sejnowski, T.J., Gage, F.H. Running enhances neurogenesis, learning, and long-term potentiation in mice. (1999) Proceedings of the National Academy of Sciences of the United States of America. 96(23):13427-31.

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McKeown, M.J., Humphries, C., Iragui, V., Sejnowski, T.J. Spatially fixed patterns account for the spike and wave features in absence seizures. (1999) Brain Topogr. 12(2):107-16.

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Donato, G., Bartlett, M.S., Hager, J.C., Ekman, P., Sejnowski, T.J. Classifying Facial Actions. (1999) IEEE Trans Pattern Anal Mach Intell. 21(10):974. DOI: 10.1109/34.799905

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Lehky, S.R., Sejnowski, T.J. Seeing white: Qualia in the context of decoding population codes. (1999) Neural Computation. 11(6):1261-80.

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Makeig, S., Westerfield, M., Townsend, J., Jung, T.P., Courchesne, E., Sejnowski, T.J. Functionally independent components of early event-related potentials in a visual spatial attention task. (1999) Philos. Trans. R. Soc. Lond., B, Biol. Sci. 354(1387):1135-44. DOI: 10.1098/rstb.1999.0469

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Zhang, K., Sejnowski, T.J. A theory of geometric constraints on neural activity for natural three-dimensional movement. (1999) Journal of Neuroscience. 19(8):3122-45.

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Makeig, S., Westerfield, M., Jung, T.P., Covington, J., Townsend, J., Sejnowski, T.J., Courchesne, E. Functionally independent components of the late positive event-related potential during visual spatial attention. (1999) Journal of Neuroscience. 19(7):2665-80.

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Bartlett, M.S., Hager, J.C., Ekman, P., Sejnowski, T.J. Measuring facial expressions by computer image analysis. (1999) Psychophysiology. 36(2):253-63.

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Bazhenov, M., Timofeev, I., Steriade, M., Sejnowski, T.J. Self-sustained rhythmic activity in the thalamic reticular nucleus mediated by depolarizing GABAA receptor potentials. (1999) Nature Neuroscience. 2(2):168-74. DOI: 10.1038/5729

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Lee, T.W., Girolami, M., Sejnowski, T.J. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. (1999) Neural Computation. 11(2):417-41.

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Ernst, U., Pawelzik, K., Tsodyks, M., Sejnowski, T.J. Relation between retinotopical and orientation maps in visual cortex. (1999) Neural Computation. 11(2):375-9.

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Zhang, K., Sejnowski, T.J. Neuronal tuning: To sharpen or broaden? (1999) Neural Computation. 11(1):75-84.

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Destexhe, A., McCormick, D.A., Sejnowski, T.J. Thalamic and thalamocortical mechanisms underlying 3 Hz spike-and-wave discharges. (1999) Progress in Brain Research. 121:289-307.

Pouget, A., Deneve, S., Sejnowski, T.J. Frames of reference in hemineglect: a computational approach. (1999) Progress in Brain Research. 121:81-97.

McKeown, M.J., Sejnowski, T.J. Independent component analysis of fMRI data: examining the assumptions. (1998) Hum Brain Mapp. 6(5-6):368-72.

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McKeown, M.J., Humphries, C., Achermann, P., Borbély, A.A., Sejnowski, T.J. A new method for detecting state changes in the EEG: exploratory application to sleep data. (1998) Journal of Sleep Research. 7 Suppl 1:48-56.

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Bartlett, M.S., Sejnowski, T.J. Learning viewpoint-invariant face representations from visual experience in an attractor network. (1998) Network. 9(3):399-417.

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Bazhenov, M., Timofeev, I., Steriade, M., Sejnowski, T.J. Computational models of thalamocortical augmenting responses. (1998) Journal of Neuroscience. 18(16):6444-65.

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McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.P., Kindermann, S.S., Bell, A.J., Sejnowski, T.J. Analysis of fMRI data by blind separation into independent spatial components. (1998) Hum Brain Mapp. 6(3):160-88.

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Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

Lin, J.K., Pawelzik, K., Ernst, U., Sejnowski, T.J. Irregular synchronous activity in stochastically-coupled networks of integrate-and-fire neurons. (1998) Network. 9(3):333-44.

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Sejnowski, T.J. Neurobiology. Making smooth moves. (1998) Nature. 394(6695):725-6. DOI: 10.1038/29404

Bazhenov, M., Timofeev, I., Steriade, M., Sejnowski, T.J. Cellular and network models for intrathalamic augmenting responses during 10-Hz stimulation. (1998) Journal of Neurophysiology. 79(5):2730-48.

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Gray, M.S., Pouget, A., Zemel, R.S., Nowlan, S.J., Sejnowski, T.J. Reliable disparity estimation through selective integration. (1998) Visual Neuroscience. 15(3):511-28.

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Moortgat, K.T., Keller, C.H., Bullock, T.H., Sejnowski, T.J. Submicrosecond pacemaker precision is behaviorally modulated: the gymnotiform electromotor pathway. (1998) Proceedings of the National Academy of Sciences of the United States of America. 95(8):4684-9.

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Zhang, K., Ginzburg, I., McNaughton, B.L., Sejnowski, T.J. Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. (1998) Journal of Neurophysiology. 79(2):1017-1044.

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Physical variables such as the orientation of a line in the visual field or the location of the body in space are coded as activity levels in populations of neurons. Reconstruction or decoding is an inverse problem in which the physical variables are estimated from observed neural activity. Reconstruction is useful first in quantifying how much information about the physical variables is present in the population and, second, in providing insight into how the brain might use distributed representations in solving related computational problems such as visual object recognition and spatial navigation. Two classes of reconstruction methods, namely, probabilistic or Bayesian methods and basis function methods, are discussed. They include important existing methods as special cases, such as population vector coding, optimal linear estimation, and template matching. As a representative example for the reconstruction problem, different methods were applied to multi-electrode spike train data from hippocampal place cells in freely moving rats. The reconstruction accuracy of the trajectories of the rats was compared for the different methods. Bayesian methods were especially accurate when a continuity constraint was enforced, and the best errors were within a factor of two of the information-theoretic limit on how accurate any reconstruction can be and were comparable with the intrinsic experimental errors in position tracking. In addition, the reconstruction analysis uncovered some interesting aspects of place cell activity, such as the tendency for erratic jumps of the reconstructed trajectory when the animal stopped running. In general, the theoretical values of the minimal achievable reconstruction errors quantify how accurately a physical variable is encoded in the neuronal population in the sense of mean square error, regardless of the method used for reading out the information. One related result is that the theoretical accuracy is independent of the width of the Gaussian tuning function only in two dimensions. Finally, all the reconstruction methods considered in this paper can be implemented by a unified neural network architecture, which the brain feasibly could use to solve related problems.

McKeown, M.J., Jung, T.P., Makeig, S., Brown, G., Kindermann, S.S., Lee, T.W., Sejnowski, T.J. Spatially independent activity patterns in functional MRI data during the stroop color-naming task. (1998) Proceedings of the National Academy of Sciences of the United States of America. 95(3):803-810.

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Zemel, R.S., Sejnowski, T.J. A model for encoding multiple object motions and self-motion in area MST of primate visual cortex. (1998) Journal of Neuroscience. 18(1):531-547.

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Berns, G.S., Sejnowski, T.J. A computational model of how the basal ganglia produce sequences. (1998) Journal of Cognitive Neuroscience. 10(1):108-121.

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Quartz, S.R., Sejnowski, T.J. The neural basis of cognitive development: a constructivist manifesto. (1997) Behavioral and Brain Sciences. 20(4):537-56; discussion 556-96.

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Bell, A.J., Sejnowski, T.J. The "independent components" of natural scenes are edge filters. (1997) Vision Research. 37(23):3327-3338.

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Pouget, A., Sejnowski, T.J. A new view of hemineglect based on the response properties of parietal neurones. (1997) Philos. Trans. R. Soc. Lond., B, Biol. Sci. 352(1360):1449-1459. DOI: 10.1098/rstb.1997.0131

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Tang, A.C., Bartels, A.M., Sejnowski, T.J. Effects of cholinergic modulation on responses of neocortical neurons to fluctuating input. (1997) Cerebral Cortex . 7(6):502-509.

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Makeig, S., Jung, T.P., Bell, A.J., Ghahremani, D., Sejnowski, T.J. Blind separation of auditory event-related brain responses into independent components. (1997) Proceedings of the National Academy of Sciences of the United States of America. 94(20):10979-10984.

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Ritz, R., Sejnowski, T.J. Synchronous oscillatory activity in sensory systems: new vistas on mechanisms. (1997) Current Opinion in Neurobiology. 7(4):536-546.

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Spitzer, N.C., Sejnowski, T.J. Biological information processing: bits of progress. (1997) Science. 277(5329):1060-1061.

Tsodyks, M.V., Skaggs, W.E., Sejnowski, T.J., McNaughton, B.L. Paradoxical effects of external modulation of inhibitory interneurons. (1997) Journal of Neuroscience. 17(11):4382-4388.

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Murthy, V.N., Sejnowski, T.J., Stevens, C.F. Heterogeneous release properties of visualized individual hippocampal synapses. (1997) Neuron. 18(4):599-612.

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Pouget, A., Sejnowski, T.J. Spatial transformations in the parietal cortex using basis functions. (1997) Journal of Cognitive Neuroscience. 9(2):222-237. DOI: 10.1162/jocn.1997.9.2.222

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Contreras, D., Destexhe, A., Sejnowski, T.J., Steriade, M. Spatiotemporal patterns of spindle oscillations in cortex and thalamus. (1997) Journal of Neuroscience. 17(3):1179-1196.

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Spindle oscillations (7-14 Hz) appear in the thalamus and cortex during early stages of sleep. They are generated by the combination of intrinsic properties and connectivity patterns of thalamic neurons and distributed to cortical territories by thalamocortical axons. The corticothalamic feedback is a major factor in producing coherent spatiotemporal maps of spindle oscillations in widespread thalamic territories. Here we have investigated the spatiotemporal patterns of spontaneously occurring and evoked spindles by means of multisite field potential and unit recordings in intact cortex and decorticated animals. We show that (1) spontaneous spindle oscillations are synchronized over large cortical areas during natural sleep and barbiturate anesthesia; (2) under barbiturate anesthesia, the cortical coherence is not disrupted by transection of intracortical synaptic linkages; (3) in intact cortex animals, spontaneously occurring barbiturate spindle sequences occur nearly simultaneously over widespread thalamic territories; (4) in the absence of cortex, the spontaneous spindle oscillations throughout the thalamus are less organized, but the local coherence (within 2-4 mm) is still maintained; and (5) spindling propagation is observed in intact cortex animals only when elicited by low intensity cortical stimulation, applied shortly before the initiation of a spontaneous spindle sequence; propagation velocities are between 1 and 3 mm/sec, measured in the anteroposterior axis of the thalamus; increasing the intensity of cortical stimulation triggers spindle oscillations, which start simultaneously in all leads. We propose that, in vivo, the coherence of spontaneous spindle oscillations in corticothalamic networks is attributable to the combined action of continuous background corticothalamic input initiating spindle sequences in several thalamic sites at the same time and divergent corticothalamic and intrathalamic connectivity.

Sejnowski, T.J. The year of the dendrite. (1997) Science. 275(5297):178-179.

Jung, T.P., Makeig, S., Stensmo, M., Sejnowski, T.J. Estimating alertness from the EEG power spectrum. (1997) IEEE Trans Biomed Eng. 44(1):60-69. DOI: 10.1109/10.553713

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Contreras, D., Destexhe, A., Sejnowski, T.J., Steriade, M. Control of spatiotemporal coherence of a thalamic oscillation by corticothalamic feedback. (1996) Science. 274(5288):771-774.

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Destexhe, A., Bal, T., McCormick, D.A., Sejnowski, T.J. Ionic mechanisms underlying synchronized oscillations and propagating waves in a model of ferret thalamic slices. (1996) Journal of Neurophysiology. 76(3):2049-2070.

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1. A network model of thalamocortical (TC) and thalamic reticular (RE) neurons was developed based on electrophysiological measurements in ferret thalamic slices. Single-compartment TC and RE cells included voltage- and calcium-sensitive currents described by Hodgkin-Huxley type of kinetics. Synaptic currents were modeled by kinetic models of alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), gamma-aminobutyric acid-A (GABAA) and GABAB receptors. 2. The model reproduced successfully the characteristics of spindle and slow bicuculline-induced oscillations observed in vitro. The characteristics of these two types of oscillations depended on both the intrinsic properties of TC and RE cells and their pattern of interconnectivity. 3. The oscillations were organized by the reciprocal recruitment between TC and RE cells, due to their manual connectivity and bursting properties. TC cells elicited AMPA-mediated excitatory postsynaptic potentials (EPSPs) in RE cells, whereas RE cells elicited a mixture of GABAA and GABAB inhibitory postsynaptic potentials (IPSPs) in TC cells. Because of the presence of a T current, sufficiently strong EPSPs could elicit a burst in RE cells, and TC cells could generate a rebound burst following GABAergic IPSPs. Under these conditions, interaction between the TC and RE cells produced sustained oscillations. 4. In the absence of spontaneous oscillation in any cell, the TC-RE network remained quiescent. Spindle oscillations with a frequency of 9-11 Hz could be initiated by stimulation of either TC or RE neurons. A few spontaneously oscillating TC neurons recruited the entire network model into a "waxing-and waning" oscillation. These "initiator" cells could be an extremely small proportion of TC cells. 5. In intracellular recordings, TC cells display a reduced ability for burst firing after a sequence of bursts. The "waning" phase of spindles was reproduced in the network model by assuming an activity-dependent upregulation of Ih operating via a calcium-binding protein in TC cells, as shown previously in a two-cell model. 6. Following the global suppression of GABAA inhibition, the disinhibited RE cells produced prolonged burst discharges that elicited strong GABAB-mediated currents in TC cells. The enhancement of slow IPSPs in TC cells was also due to cooperativity in the activation of GABAB-mediated current. These slow IPSPs recruited TC and RE cells into slower waxing-and-waning oscillations (3-4 HZ) that were even more highly synchronized. 7. Local axonal arborization of the TC to RE and RE to TC projections allowed oscillations to propagate through the network. An oscillation starting at a single focus induced a propagating wavefront as more cells were recruited progressively. The waning of the oscillation also propagated due to upregulation of Ih in TC cells, leading to waves of spindle activity as observed in experiments. 8. The spatiotemporal properties of propagating waves in the model were highly dependent on the intrinsic properties of TC cells. The spatial pattern of spiking activity was markedly different for spindles compared with bicuculline-induced oscillations and depended on the rebound burst behavior of TC cells. The upregulation of Ih produced a refractory period so that colliding spindle waves merged into a single oscillation and extinguished. Finally, reducing the Ih conductance led to sustained oscillations. 9. Two key properties of cells in the thalamic network may account for the initiation, propagation, and termination of spindle oscillations, the activity-dependent upregulation of Ih in TC cells, and the localized axonal projections between TC and RE cells. In addition, the model predicts that a nonlinear stimulus dependency of GABAB responses accounts for the genesis of prolonged synchronized discharges following block of GABAA receptors.

Sejnowski, T.J. Synapses get smarter. (1996) Nature. 382(6594):759-760. DOI: 10.1038/382759a0

Prank, K., Kloppstech, M., Nowlan, S.J., Sejnowski, T.J., Brabant, G. Random Secretion of Growth Hormone in Humans. (1996) Phys. Rev. Lett. 77(9):1909-1911. DOI: 10.1103/PhysRevLett.77.1909

Mainen, Z.F., Sejnowski, T.J. Influence of dendritic structure on firing pattern in model neocortical neurons. (1996) Nature. 382(6589):363-366. DOI: 10.1038/382363a0

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Prank, K., Kloppstech, M., Nowlan, S.J., Sejnowski, T.J., Brabant, G. Self-organized segmentation of time series: separating growth hormone secretion in acromegaly from normal controls. (1996) Biophysical Journal. 70(6):2540-2547. DOI: 10.1016/S0006-3495(96)79825-9

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Tsodyks, M.V., Skaggs, W.E., Sejnowski, T.J., McNaughton, B.L. Population dynamics and theta rhythm phase precession of hippocampal place cell firing: a spiking neuron model. (1996) Hippocampus. 6(3):271-280. DOI: 10.1002/(SICI)1098-1063(1996)6:3<271::AID-HIPO5

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Bell, A.J., Sejnowski, T.J. Learning the higher-order structure of a natural sound. (1996) Network. 7(2):261-267. DOI: 10.1088/0954-898X/7/2/005

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Montague, P.R., Dayan, P., Sejnowski, T.J. A framework for mesencephalic dopamine systems based on predictive Hebbian learning. (1996) Journal of Neuroscience. 16(5):1936-1947.

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Lytton, W.W., Destexhe, A., Sejnowski, T.J. Control of slow oscillations in the thalamocortical neuron: a computer model. (1996) Neuroscience. 70(3):673-684.

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We investigated computer models of a single thalamocortical neuron to assess the interaction of intrinsic voltage-sensitive channels and cortical synaptic input in producing the range of oscillation frequencies observed in these cells in vivo. A morphologically detailed model with Hodgkin-Huxley-like ion channels demonstrated that intrinsic properties would be sufficient to readily produce 3 to 6 Hz oscillations. Hyperpolarization of the model cell reduced its oscillation frequency monotonically whether through current injection or modulation of a potassium conductance, simulating the response to a neuromodulatory input. We performed detailed analysis of highly reduced models to determine the mechanism of this frequency control. The interburst interval was controlled by two different mechanisms depending on whether or not the pacemaker current, IH, was present. In the absence of IH, depolarization during the interburst interval occurred at the same rate with different current injections. The voltage difference from the nadir to threshold for the low-threshold calcium current, IT, determined the interburst interval. In contrast, with IH present, the rate of depolarization depended on injected current. With the full model, simulated repetitive cortical synaptic input entrained oscillations up to approximately double the natural frequency. Cortical input readily produced phase resetting as well. Our findings suggest that neither ascending brainstem control altering underlying hyperpolarization, nor descending drive by repetitive cortical inputs, would alone be sufficient to produce the range of oscillation frequencies seen in thalamocortical neurons. Instead, intrinsic neuronal mechanisms would dominate for generating the delta range (0.5-4 Hz) oscillations seen during slow wave sleep, whereas synaptic interactions with cortex and the thalamic reticular nucleus would be required for faster oscillations in the frequency range of spindling (7-14 Hz).

Destexhe, A., Contreras, D., Steriade, M., Sejnowski, T.J., Huguenard, J.R. In vivo, in vitro, and computational analysis of dendritic calcium currents in thalamic reticular neurons. (1996) Journal of Neuroscience. 16(1):169-185.

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Mainen, Z.F., Joerges, J., Huguenard, J.R., Sejnowski, T.J. A model of spike initiation in neocortical pyramidal neurons. (1995) Neuron. 15(6):1427-1439.

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Gray, M.S., Lawrence, D.T., Golomb, B.A., Sejnowski, T.J. A perceptron reveals the face of sex. (1995) Neural Computation. 7(6):1160-1164.

Bell, A.J., Sejnowski, T.J. An information-maximization approach to blind separation and blind deconvolution. (1995) Neural Computation. 7(6):1129-1159.

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Destexhe, A., Sejnowski, T.J. G protein activation kinetics and spillover of gamma-aminobutyric acid may account for differences between inhibitory responses in the hippocampus and thalamus. (1995) Proceedings of the National Academy of Sciences of the United States of America. 92(21):9515-9519.

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Montague, P.R., Dayan, P., Person, C., Sejnowski, T.J. Bee foraging in uncertain environments using predictive hebbian learning. (1995) Nature. 377(6551):725-728. DOI: 10.1038/377725a0

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Sejnowski, T.J. Neural networks. Sleep and memory. (1995) Current Biology. 5(8):832-834.

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Sejnowski, T.J. Pattern recognition. Time for a new neural code? (1995) Nature. 376(6535):21-22. DOI: 10.1038/376021a0

Mainen, Z.F., Sejnowski, T.J. Reliability of spike timing in neocortical neurons. (1995) Science. 268(5216):1503-1506.

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Prank, K., Nowlan, S.J., Harms, H.M., Kloppstech, M., Brabant, G., Hesch, R.D., Sejnowski, T.J. Time series prediction of plasma hormone concentration. Evidence for differences in predictability of parathyroid hormone secretion between osteoporotic patients and normal controls. (1995) Journal of Clinical Investigation. 95(6):2910-2919. DOI: 10.1172/JCI117998

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du Lac, S., Raymond, J.L., Sejnowski, T.J., Lisberger, S.G. Learning and memory in the vestibulo-ocular reflex. (1995) Annual Review of Neuroscience. 18:409-441. DOI: 10.1146/annurev.ne.18.030195.002205

Jester, J.M., Campbell, L.W., Sejnowski, T.J. Associative EPSP--spike potentiation induced by pairing orthodromic and antidromic stimulation in rat hippocampal slices. (1995) Journal of Physiology. 484 ( Pt 3:689-705.

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Nowlan, S.J., Sejnowski, T.J. A selection model for motion processing in area MT of primates. (1995) Journal of Neuroscience. 15(2):1195-1214.

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Destexhe, A., Contreras, D., Sejnowski, T.J., Steriade, M. Modeling the control of reticular thalamic oscillations by neuromodulators. (1994) Neuroreport. 5(17):2217-2220.

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Destexhe, A., Mainen, Z.F., Sejnowski, T.J. Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. (1994) Journal of Computational Neuroscience. 1(3):195-230.

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Destexhe, A., Contreras, D., Sejnowski, T.J., Steriade, M. A model of spindle rhythmicity in the isolated thalamic reticular nucleus. (1994) Journal of Neurophysiology. 72(2):803-818.

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1. The oscillatory properties of the isolated reticular (RE) thalamus were modeled with the use of compartmental models of RE cells. Hodgkin-Huxley type kinetic models of ionic channels were derived from voltage- and current-clamp data from RE cells. Interactions between interconnected RE cells were simulated with the use of a kinetic model of gamma-aminobutyric acid (GABA) inhibitory synapses. 2. The intrinsic bursting properties of RE cells in the model were due to the presence of a low-threshold Ca2+ current and two Ca(2+)-activated currents. The properties of these model RE cells were compared with RE neurons recorded intracellularly in vivo in cats. 3. Model RE cells densely interconnected with GABAA synapses produced synchronous oscillations at a frequency close to that of spindles (7-14 Hz). Networks of RE neurons organized in a two-dimensional array with only proximal connectivity also exhibited synchronized oscillations in the spindle range. In addition, the proximally connected network showed periods of high and low synchronicity, giving rise to waxing and waning oscillations in the population of RE cells. 4. The spatiotemporal behavior of the network was investigated during waxing and waning oscillations. The waxing and waning emerged as an alternation between periods of desynchronized and synchronized activity, corresponding to periods of irregular and coherent spatial activity. During synchronized periods, the network displayed propagating coherent waves of synchronous activity that had a tendency to form spirals. 5. Networks of model RE neurons fully connected through GABAB synapses exhibited perfectly synchronous oscillations at lower frequencies (0.5-1 Hz), but two-dimensional networks with proximal GABAB connectivity failed to synchronize. 6. These simulations demonstrate that networks of model neurons that include the main intrinsic currents found in RE cells can generate waxing and waning oscillatory activity similar to the spindle rhythmicity observed in the isolated RE nucleus in vivo. The model reveals the interplay between the intrinsic rhythmic properties of RE cells and the fast synaptic interactions in organizing synchronized rhythmicity.

Bush, P.C., Sejnowski, T.J. Effects of inhibition and dendritic saturation in simulated neocortical pyramidal cells. (1994) Journal of Neurophysiology. 71(6):2183-2193.

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1. We have used compartmental models of reconstructed pyramidal neurons from layers 2 and 5 of cat visual cortex to investigate the nonlinear summation of excitatory synaptic input and the effectiveness of inhibitory input in countering this excitation. 2. In simulations that match the conditions of a recent experiment, dendritic saturation was significant for physiological levels of synaptic activation: a compound excitatory postsynaptic potential (EPSP) electrically evoked during a depolarization caused by physiological synaptic activation was decreased by up to 80% compared with an EPSP evoked at rest. 3. Synaptic inhibition must be coactivated with excitation to quantitatively match the experimental results. The experimentally observed coactivation of inhibition with excitation produced additional current shunts that amplified the decrease in test EPSP amplitude. About 30% of the experimentally observed decrease in EPSP amplitude was caused by decreases in input resistance (Rin) due to synaptic conductance changes; a reduced driving force accounted for the remaining decrease. 4. The amount of inhibition was then increased by nearly an order of magnitude, to approximately 10% of the total number of inhibitory synapses on a typical cortical pyramidal cell. The sustained firing of this many inhibitory inputs was sufficient to completely suppress the firing of a neuron receiving strong excitatory input. However, this level of inhibition produced a very large reduction in Rin. Such large reductions in Rin have not been observed experimentally, suggesting that inhibition in cortex does not act to veto (shunt) strong, sustained excitatory input (of order 100 ms). 5. We propose instead that strong, transient activation (< 10 ms) of a neuron's inhibitory inputs, sufficient to briefly prevent firing, is used to shape the temporal structure of the cell's output spike train. Specifically, cortical inhibition may serve to synchronize the firing of groups of pyramidal cells during optimal stimulation.

Pouget, A., Sejnowski, T.J. A neural model of the cortical representation of egocentric distance. (1994) Cerebral Cortex. 4(3):314-329.

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Montague, P.R., Sejnowski, T.J. The predictive brain: temporal coincidence and temporal order in synaptic learning mechanisms. (1994) Learning & Memory. 1(1):1-33.

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Destexhe, A., McCormick, D.A., Sejnowski, T.J. A model for 8-10 Hz spindling in interconnected thalamic relay and reticularis neurons. (1993) Biophysical Journal. 65(6):2473-2477. DOI: 10.1016/S0006-3495(93)81297-9

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Steriade, M., McCormick, D.A., Sejnowski, T.J. Thalamocortical oscillations in the sleeping and aroused brain. (1993) Science. 262(5134):679-685.

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Destexhe, A., Babloyantz, A., Sejnowski, T.J. Ionic mechanisms for intrinsic slow oscillations in thalamic relay neurons. (1993) Biophysical Journal. 65(4):1538-1552. DOI: 10.1016/S0006-3495(93)81190-1

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Berns, G.S., Dayan, P., Sejnowski, T.J. A correlational model for the development of disparity selectivity in visual cortex that depends on prenatal and postnatal phases. (1993) Proceedings of the National Academy of Sciences of the United States of America. 90(17):8277-8781.

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Lockery, S.R., Sejnowski, T.J. The computational leech. (1993) Trends in Neurosciences. 16(7):283-290.

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Lockery, S.R., Sejnowski, T.J. A lower bound on the detectability of nonassociative learning in the local bending reflex of the medicinal leech. (1993) Behav. Neural Biol. 59(3):208-224.

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Pouget, A., Fisher, S.A., Sejnowski, T.J. Egocentric spaw representation in early vision. (1993) Journal of Cognitive Neuroscience. 5(2):150-161. DOI: 10.1162/jocn.1993.5.2.150

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Bush, P.C., Sejnowski, T.J. Reduced compartmental models of neocortical pyramidal cells. (1993) Journal of Neuroscience Methods. 46(2):159-166.

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Lisberger, S.G., Sejnowski, T.J. Motor learning in a recurrent network model based on the vestibulo-ocular reflex. (1992) Nature. 360(6400):159-161. DOI: 10.1038/360159a0

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Lockery, S.R., Sejnowski, T.J. Distributed processing of sensory information in the leech. III. A dynamical neural network model of the local bending reflex. (1992) Journal of Neuroscience. 12(10):3877-3895.

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Lehky, S.R., Sejnowski, T.J., Desimone, R. Predicting responses of nonlinear neurons in monkey striate cortex to complex patterns. (1992) Journal of Neuroscience. 12(9):3568-3581.

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Lytton, W.W., Sejnowski, T.J. Computer model of ethosuximide's effect on a thalamic neuron. (1992) Ann. Neurol. 32(2):131-139. DOI: 10.1002/ana.410320204

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Sejnowski, T.J. Models of vision. (1992) Science. 257(5070):687-688. DOI: 10.1126/science.257.5070.687

Wathey, J.C., Lytton, W.W., Jester, J.M., Sejnowski, T.J. Computer simulations of EPSP-spike (E-S) potentiation in hippocampal CA1 pyramidal cells. (1992) Journal of Neuroscience. 12(2):607-618.

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Holliday, J., Adams, R.J., Sejnowski, T.J., Spitzer, N.C. Calcium-induced release of calcium regulates differentiation of cultured spinal neurons. (1991) Neuron. 7(5):787-796.

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Lytton, W.W., Sejnowski, T.J. Simulations of cortical pyramidal neurons synchronized by inhibitory interneurons. (1991) Journal of Neurophysiology. 66(3):1059-1079.

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1. The interaction between inhibitory interneurons and cortical pyramidal neurons was studied by use of computer simulations to test whether inhibitory interneurons could assist in phase-locking postsynaptic cells. Two models were used: a simplified model, which included only 3 membrane channels, and a detailed 11-channel model. 2. The 11-channel model included most of the ion channels known to be present in neocortical pyramidal neurons as well as calcium diffusion and other membrane mechanisms. The kinetics for the channels were obtained from voltage-clamp studies in a variety of preparations. The parameters were then adjusted to produce repetitive bursting similar to that seen in some cortical pyramidal cells entrained during visual stimulation. 3. Phase-locking to a train of inhibitory postsynaptic potentials (IPSPs) located on or near the soma was observed in the 3-channel model cell subjected to random synaptic bombardment. In the 11-channel model, phase-locking due to multiple IPSPs was compared with phase-locking due to multiple excitatory postsynaptic potentials (EPSPs). Phase-locking began to occur when 20% of the IPSPs (20/100) or 40% of the EPSPs (4,000/10,000) were synchronized. The exact percentages differed with different 11-channel models, but either EPSPs or IPSPs would generally produce entrainment with approximately 40% synchronization. Thus 40 inhibitory boutons had an effect equivalent to 4,000 excitatory boutons in producing phase-locking. 4. Phase-locking with IPSPs in these models was possible because the IPSPs could cause either an increase or a decrease in firing rate over a limited range. The IPSPs served a modulatory role, increasing the rate of firing in some cases and decreasing it in others, depending on the state of the cell. 5. We examined frequency entrainment by IPSPs. In the 3-channel model, frequency entrainment of a postsynaptic cell was observed with a rapid train of strong (20-100 nS), brief, compound IPSPs. A 40-Hz compound IPSP train of 60 nS entrained cells having initial firing rates between 32 and 47 Hz. Below this range, cells could be partially entrained. Above the range, entrainment would fail. Frequency entrainment in the 3-channel model generally occurred on the first cycle after onset of the IPSPs. 6. Phase-locking and frequency entrainment were less robust in the 11-channel model. This was partly because bursts rather than individual spikes were being entrained. A 40-Hz, 90-nS compound IPSP train entrained a model cell upward from 34 Hz. Downward frequency entrainment also occurred.(ABSTRACT TRUNCATED AT 400 WORDS)

Bush, P.C., Sejnowski, T.J. Simulations of a Reconstructed Cerebellar Purkinje Cell Based on Simplified Channel Kinetics. (1991) Neural Computation. 3(3):321-332. DOI: 10.1162/neco.1991.3.3.321

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Sejnowski, T.J. Neuroscience. Back together again. (1991) Nature. 352(6337):669-670. DOI: 10.1038/352669a0

Stanton, P.K., Chattarji, S., Sejnowski, T.J. 2-Amino-3-phosphonopropionic acid, an inhibitor of glutamate-stimulated phosphoinositide turnover, blocks induction of homosynaptic long-term depression, but not potentiation, in rat hippocampus. (1991) Neurosci. Lett. 127(1):61-66.

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Sejnowski, T.J. Synaptic mechanisms for long-term depression. (1991) Current Biology. 1(1):38-40.

Qian, N., Sejnowski, T.J. When is an inhibitory synapse effective? (1990) Proceedings of the National Academy of Sciences of the United States of America. 87(20):8145-8149.

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Lehky, S.R., Sejnowski, T.J. Neural model of stereoacuity and depth interpolation based on a distributed representation of stereo disparity. (1990) Journal of Neuroscience. 10(7):2281-2299.

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Lehky, S.R., Sejnowski, T.J. Neural network model of visual cortex for determining surface curvature from images of shaded surfaces. (1990) Proceedings of the Royal Society of London. Series B, Biological sciences. 240(1298):251-278.

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Lehky, S.R., Pouget, A., Sejnowski, T.J. Neural models of binocular depth perception. (1990) Cold Spring Harbor Symposia on Quantitative Biology. 55:765-777.

Chattarji, S., Stanton, P.K., Sejnowski, T.J. Commissural synapses, but not mossy fiber synapses, in hippocampal field CA3 exhibit associative long-term potentiation and depression. (1989) Brain Research. 495(1):145-50.

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Stanton, P.K., Sejnowski, T.J. Associative long-term depression in the hippocampus induced by hebbian covariance. (1989) Nature. 339(6221):215-8. DOI: 10.1038/339215a0

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Churchland, P.S., Sejnowski, T.J. Perspectives on cognitive neuroscience. (1988) Science. 242(4879):741-5.

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Sejnowski, T.J., Koch, C., Churchland, P.S. Computational neuroscience. (1988) Science. 241(4871):1299-306.

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Qian, N., Sejnowski, T.J. Predicting the secondary structure of globular proteins using neural network models. (1988) Journal of Molecular Biology. 202(4):865-84.

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Lehky, S.R., Sejnowski, T.J. Network model of shape-from-shading: neural function arises from both receptive and projective fields. (1988) Nature. 333(6172):452-4. DOI: 10.1038/333452a0

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Sejnowski, T.J. Neural populations revealed. (1988) Nature. 332(6162):308. DOI: 10.1038/332308a0

Kienker, P.K., Sejnowski, T.J., Hinton, G.E., Schumacher, L.E. Separating figure from ground with a parallel network. (1986) Perception. 15(2):197-216. DOI: 10.1068/p150197

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Ballard, D.H., Hinton, G.E., Sejnowski, T.J. Parallel visual computation. (1983) Nature. 306(5938):21-6.

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Kuffler, S.W., Sejnowski, T.J. Peptidergic and muscarinic excitation at amphibian sympathetic synapses. (1983) Journal of Physiology. 341:257-78.

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A single-electrode voltage clamp was used to study the slow muscarinic and late slow peptidergic excitatory post-synaptic currents (e.p.s.c.s) in B cells of the paravertebral sympathetic ganglia of the bull-frog. Conductance decreases were measured during peptidergic e.p.s.c.s in nearly all cells at clamped potentials near the resting level. In about half of the cells the size of the peptidergic e.p.s.c.s increased with hyperpolarization and in some of these cells conductance increases were found at hyperpolarized levels. In the remaining cells conductance decreases occurred at all levels of membrane potential tested, and in a few of these the polarity of the e.p.s.c.s reversed at hyperpolarized potentials. A similar diversity was observed among muscarinic e.p.s.c.s. At least two simple ionic mechanisms are required to explain the heterogeneous voltage dependencies observed: a conductance decrease primarily to K+ that dominates at depolarized potentials and a conductance increase to other ions that is more prominent at hyperpolarized potentials. The proportion of these two mechanisms appears to differ among B cells. The two slow e.p.s.c.s recorded in the same neurone had the same voltage dependence and were accompanied by the same conductance changes in each of eight cells despite differences between cells. The muscarinic e.p.s.c. was reduced during the peptidergic e.p.s.c. in each of twenty-five neurones tested over a range of membrane potentials. Externally-applied luteinizing hormone releasing hormone (LHRH) produced currents with the same voltage dependence and conductance changes as the nerve-evoked peptidergic e.p.s.c. in each of fifteen cells tested. Bethanechol, a muscarinic agonist, and LHRH produced currents with the same voltage dependence and conductance changes in each of the twelve cells studied. In several cells a saturating response to a prolonged application of LHRH completely occluded the response to bethanechol, and vice versa. Slow currents were recorded from dissociated cell bodies in response to bethanechol and LHRH; these responses exhibited the same diversity of voltage dependence and conductance changes as was observed in intact ganglia. Activation of muscarinic and peptidergic receptors may control shared ionic mechanisms in single ganglion cells.

Sejnowski, T.J., Yodlowski, M.L. A freeze-fracture study of the skate electroreceptor. (1982) Journal of Neurocytology. 11(6):897-912.

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Sejnowski, T.J. Peptidergic synaptic transmission in sympathetic ganglia. (1982) Federation Proceedings. 41(13):2923-8.

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Paton, J.A., Kelley, D.B., Sejnowski, T.J., Yodlowski, M.L. Mapping the auditory central nervous system of Xenopus laevis with 2-deoxyglucose autoradiography. (1982) Brain Research. 249(1):15-22.

Reingold, S.C., Sejnowski, T.J., Gelperin, A., Kelley, D.B. [3H]-2-deoxyglucose autoradiography in a molluscan nervous system. (1981) Brain Research. 208(2):416-20.

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Sejnowski, T.J., Reingold, S.C., Kelley, D.B., Gelperin, A. Localization of[3H]-2-deoxyglucose in single molluscan neurones. (1980) Nature. 287(5781):449-51.

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Sejnowski, T.J. Statistical constraints on synaptic plasticity. (1977) Journal of Theoretical Biology. 69(2):385-9.

Sejnowski, T.J. Storing covariance with nonlinearly interacting neurons. (1977) Journal of Mathematical Biology. 4(4):303-21.

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Sejnowski, T.J. On the stochastic dynamics of neuronal interaction. (1976) Biological Cybernetics. 22(4):203-11.

Sejnowski, T.J. On global properties of neuronal interaction. (1976) Biol Cybern. 22(2):85-95.

Terrence Sejnowski, PhD

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