Research focus: Our group seeks to understand high-level cognitive function from the bottom-up: how neural dynamics and connectivity constraints drive the emergence of function; the characterization of low-dimensional topological and geometric structures in the population dynamics of neural circuits; elucidation of the fundamental constraints and capacity of memory systems constructed from noisy, leaky neurons, and construction of biological memory architectures; biological learning rules; the existence and mechanisms underlying rigid and canonical circuits for fundamental computations like integration; the reuse of these circuits for flexible and data-efficient computation, inference, and learning.
Our tools are theoretical and numerical, ranging from the theoretical analysis of emergent dynamics and codes in neural circuits, to the quantitative and theory-driven analysis of neural data, to building simulations of single and multiple interacting circuits. We use, develop, and adapt cutting-edge approaches from mathematics, machine learning, and physics. Our approach includes working closely with collaborators on specific experimental systems. To learn more about what we do, please see our Publications.
Who we are: We seek to exhibit by example that there is no fixed mold for what a scientist looks like. There is no stereotypical or fixed path to a career involving science: each path is idiosyncratic and unique, and this idiosyncrasy is a strength that leads to new ways of looking at and solving problems. To see more about who we are, please visit our People page.
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