Salk Institute
Tatyana Sharpee
Associate Professor
Computational Neurobiology Laboratory
Tatyana Sharpee

Associate Professor
Computational Neurobiology Laboratory


Research

Our group works on theoretical principles of how the brain processes information. We are interested in how sensory processing in the brain is shaped by the animal's need to create parsimonious representations of events in the outside world. Our approaches are often derived from methods in statistical physics, mathematics, and information theory.

We also work on methods for analyzing neural data, including methods for analyzing neural responses to natural stimuli, such as a short video clip or sound recording during a stroll on a forest trail. In the past, scientists had to rely on simplified objects on a computer screen or random stimuli to garner information on how the brain processes visual information. Natural stimuli are often much better for probing neural responses than random noise stimuli. Using approaches designed to work with natural stimuli, we hope to achieve a more complete picture of how the brain processes information.

"Neurobiologists are on a perennial quest to understand how the brain codes stimuli in the environment. In the past, scientists had to rely on simplified objects on a computer screen. I try to take it a step further and analyze how brain cells respond to stimuli typical of our natural environment."

Sharpee's lab works on theoretical principles of how the brain processes information and explores how sensory processing in the brain is shaped by our need to create representations of events in the outside world. Researchers used to rely on simplified objects on a computer screen or random stimuli to garner information on how the brain processes visual information. But our brains are meant to operate in a remarkably complex and fast-changing world, so these methods offered limited insight into the brain's workings. In contrast, natural stimuli, such as a short video clip or sound recorded during a stroll on a forest trail, are often much better for probing neural responses. This is particularly true when studying intermediate and high-level neurons in the brain that translate sensory stimuli from the environment into meaningful information. These neurons rarely respond to simple patterns that are devoid of recognizable real-world objects, but they will respond to the rich ensemble of object combinations found in our environment.

Sharpee and her colleagues are developing methods to identify which features of natural stimuli cause a response in high-level neurons and how the brain prioritizes and makes sense of this information. A key obstacle to understanding how our brain functions is that neurons respond in highly nonlinear ways to complex stimuli, meaning that signals are amplified and suppressed in unexpected ways. As a result, stimulus-response relationships are extremely difficult to discern.

To address this, Sharpee's team has worked out rigorous statistical methods for modeling how visual and auditory stimuli are coded and decoded through nonlinear transformations in different brain centers. These studies are beginning to uncover the common principles of hierarchical information processing in the brain and will help us better understand how neurological disorders and injuries interrupt these crucial processes. Knowing how information moves through our sensory systems may offer new strategies for helping people with impairments, such as improving the performance of retinal implants that could help restore vision in the blind.

Lab Photo

Left to right:
Joel Kaardal, Yonatan Aljadeff, Adam Calhoun, Ramona Marchand, Tatyana Sharpee, Anirvan Nandy, Andy Briggs, Ryan Rowekamp

Selected Publications

A. Nandy, T.O. Sharpee, J. H. Reynolds, and J. Mitchel, (2013) "The fine structure of shape tuning in area V4", Neuron, 78(6), pp.1102-1115.

J. Jeanne, T.O. Sharpee and T.Q. Gentner, (2013) "Associative learning enhances population coding by inverting inter-neuronal correlation patterns", Neuron, 78 (2), pp.352-363.

T.O. Sharpee, M. Kouh, and J. H. Reynolds, (2013) "Trade-off between curvature tuning and position invariance in visual area V4", PNAS, 110 (28), pp. 11618-23.

J. Kaardal, J.D. Fitzgerald, M. J. Berry, and T.O. Sharpee, (2013) "Identifying functional bases for multidimensional neural computations", Neural Computation, 25 (7), pp.1870-1890.

M. Eickenberg, R.J. Rowekamp, M. Kouh, and T.O. Sharpee, (2012) "Characterizing Responses of Translation-Invariant Neurons to Natural Stimuli: Maximally Informative Invariant Dimensions", Neural Computation, 24 (9), pp.2384-2421.

J.D. Fitzgerald, R. J. Rowekamp, L.C. Sincich and T.O. Sharpee, (2011) "Second order dimensionality reduction using minimum and maximum mutual information models", PLoS Computational Biology, 7(10): e1002249

J.D. Fitzgerald, L.C. Sincich and T.O. Sharpee, (2011) "Minimal models of multidimensional computations", PLoS Computational Biology, 7(3): e1001111.

J. Jeanne, J. Thompson, T.O. Sharpee, and T. Gentner, (2011) "Emergence of learned categorical representations within an auditory forebrain circuit", Journal of Neuroscience, 31 (7), pp.2595-2606.

K. Imaizumi, N. Priebe, T.O. Sharpee, S. Cheung, and C.E. Schreiner, (2010) "Encoding of Temporal Information by Timing, Rate, and Place in Cat Auditory Cortex", PLoS ONE 5(7): e11531.

J. D. Fitzgerald and T. O. Sharpee, (2009) "Maximally informative pairwise interactions in networks", Phys. Rev. E, 80, 031914.

Y. Liu, C. F. Stevens, and T. O. Sharpee, (2009) "Predictable irregularities in retinal receptive fields", PNAS, 38, 16499-16504.

M. J. Kouh and T. O. Sharpee, (2009) "Estimating linear-nonlinear neural models using Renyi divergences", Network: Computation in Neural Systems, 20(2): 49-68.

L. C. Sincich, J. C. Horton, and T. O. Sharpee, (2009) "Preserving information in neural transmission", Journal of Neuroscience, 29(19), 6207-6216.

T.O. Sharpee and J.D. Victor, (2009) "Contextual modulation of V1 receptive fields depends on their spatial symmetry", Journal of Computational Neuroscience, 26(2): pp. 203-218.

T. O. Sharpee, K. D. Miller, and M. P. Stryker, (2008) "On the importance of the static nonlinearity in estimating spatiotemporal neural filters with natural stimuli", Journal of Neurophysiology 99(5): 2496-2509.

T. Sharpee and W. Bialek, (2007) "Neural decision boundaries for maximal information transmission", PLoS ONE 2(7): e646.

T. Sharpee, (2007) "Comparison of information and variance optimization strategies for characterizing neural feature selectivity", Statistics in Medicine 26 (21), pp. 4009-4031.

T. Sharpee, H. Sugihara, A. Kurgansky, S. Rebrik, M.P. Stryker, and K.D. Miller, (2006) Adaptive filtering enhances information transmission in visual cortex, Nature  439, pp. 936-942.

T. Sharpee, N. C. Rust, and W. Bialek, (2004) Analyzing neural responses to natural signals: Maximally informative dimensions, Neural Computation, 16 (2), pp. 223-250 2004.

M.I. Dykman, T. Sharpee , P.M. Platzman, (2001) "Enhancement of tunneling from a correlated 2D electron system by a many-electron Mauer-type recoil in a magnetic field", Phys. Rev. Lett. 86, pp. 2408-2411 (2001).

T. Barabash-Sharpee, M.I. Dykman, P.M. Platzman, (2000) Tunneling transverse to magnetic field, and its occurence in correlated 2D electron systems, Phys. Rev. Lett. 84 , pp. 2227-2230.

Awards and Honors

  • 2010 Helen McLoraine Developmental Chair in Neurobiology
  • 2009 W.M. Keck Foundation Research Excellence Award
  • 2009 McKnight Scholar
  • 2008 Ray Thomas Edwards Foundation Career Development Award in the Biomedical Sciences
  • Mentored Quantitative Research Career Development Award from the National Institute of Mental Health, 2004-2009
  • Alfred P. Sloan Research Fellowship 2008
  • 2008 Searle Scholar

Links

Sharpee lab

We welcome applications to join the group. For more information please contact sharpee@salk.edu.


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