韦特先进生物光子学中心

The 38 currently registered clinical trials with the keyword "adult neurogenesis" indicate growing interest in new neurons as a target for intervention. Today, we have strong evidence that adult neurogenesis is involved in hippocampal function and can contribute to brain functions in health and disease. Neurogenesis research can now ask new questions, such as (1) the identity of stem cells and their input integration for initiating neurogenesis, (2) the nature of the neurogenic niche and neurogenesis without stem cell activity, (3) the complex functionality beyond the hippocampus, and (4) evolutionary and computational theory, including neurogenic neural networks for artificial intelligence.

The spinal cord stands as a crucial nexus in the central nervous system (CNS), integrating and modulating signals that ultimately shape our everyday interactions with the world. Its gray matter is arranged into discrete laminae spanning the dorsal-ventral axis that encompass circuit-specific modalities. Concurrently, extensive interconnected interneuron networks within and between these laminae confer remarkable flexibility in the behavioral outputs for a given input. The flexibility of spinal cord information processing in light of its organized architecture makes it a particularly intriguing region to explore the neuronal computations underlying behaviors, particularly as they relate to neurological dysfunction. At the same time, astrocytes engage in highly dynamic interactions with underlying neuronal circuitries, suggesting they may add another dimension to spinal cord information processing. Technical limitations specific to the spinal cord have long limited our ability to interrogate the relationship between astrocyte-neuron interactions and ongoing spinal cord function. In this review, we highlight emerging insights-particularly those from recent in vivo studies-that illustrate astrocytes actively shape spinal cord behavioral outputs in both health and disease. We briefly review the spinal cord's neuronal organization to provide a structural foundation for assessing the relative spatial relationship between astrocyte and neuron activity as it relates to different spinal cord outputs. Within this architectural framework, we review growing evidence that spinal cord astrocytes respond to activity associated with spinal cord function and, in turn, modulate underlying neuronal circuits to alter future behavioral outputs. Moreover, we propose an overall conceptual framework for understanding circuit-specific spinal cord modulations through the lens of astrocyte-neuron interactions and underscore how it can be leveraged to uncover novel ways of targeting spinal cord disease states. Finally, we put forth key outstanding questions related to this conceptual framework and emphasize the technological advances that will facilitate future studies addressing them.

Neuromodulators are critical controllers of neural states, with dysfunctions linked to various neuropsychiatric disorders. Although many biological aspects of neuromodulation have been studied, the computational principles underlying how neuromodulation of distributed neural populations controls brain states remain unclear. In contrast to external contextual inputs, neuromodulation can act as a single scalar signal that is broadcast to a vast population of neurons. We model the modulation of synaptic weight in a recurrent neural network model and show that neuromodulators can dramatically alter the function of a network, even when highly simplified. We find that under structural constraints like those in brains, this provides a fundamental mechanism that can increase the computational capability and flexibility of a neural network. Diffuse synaptic weight modulation enables storage of multiple memories using a common set of synapses that are able to generate diverse, even diametrically opposed, behaviors. Our findings help explain how neuromodulators unlock specific behaviors by creating task-specific hyperchannels in neural activity space and motivate more flexible, compact and capable machine learning architectures.

Spatiotemporal flows of neural activity, such as traveling waves, have been observed throughout the brain since the earliest recordings; yet there is still little consensus on their functional role. Recent experiments and models have linked traveling waves to visual and physical motion, but these observations have been difficult to reconcile with standard accounts of topographically organized selectivity and feedforward receptive fields. Here, we introduce a theoretical framework that formalizes and generalizes the connection between 'motion' and flowing neural dynamics in the language of equivariant neural network theory. We consider 'motion' not only in physical or visual spaces, but also in more abstract representational spaces, and we argue that recurrent traveling-wave-like dynamics are not just useful but necessary for accurate and stable processing of any signal undergoing such motion. Formally, we show that for any non-trivial recurrent neural network to process a sequence undergoing a flow transformation (such as visual motion) in a structured equivariant manner, its hidden state dynamics must actively realize a homomorphic representation of the same flow through recurrent connectivity. In this ''spatiotemporal perspective on dynamical computation'', traveling waves and related flows are best understood as faithful dynamic representations of stimulus flows; and consequently the natural inclination of biological systems towards such dynamics may be viewed as an innate inductive bias towards efficiency and generalization in the spatiotemporally-structured dynamical world they inhabit.

The hippocampus must balance stable memory representations with internally generated sequential dynamics underlying replay, prediction and traveling waves. How hippocampal circuitry achieves both remains unclear. Here, we show that recurrent neural networks trained on continuous prediction tasks converge to a mixed-symmetry dynamical regime, in which dominant symmetric recurrence stabilizes a continuous attractor while a weaker antisymmetric component induces directed flow along it. This structure accelerates learning and supports robust replay and prediction. We further show that such symmetry breaking can arise from biologically plausible spike-timing-dependent plasticity (STDP) rules, yielding a tilted Mexican-hat connectivity profile without explicit architectural constraints. Importantly, initializing networks with CA3-like structured connectivity biases learning toward this regime, improving optimization efficiency and performance. These results suggest that hippocampal computation reflects an interaction between biologically constrained circuit structure and task-driven learning, with partial symmetry breaking providing a low-dimensional control principle for balancing stability and flow in sequence generation.