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  1. Catalyzing next-generation Artificial Intelligence through NeuroAI

    Publication

  2. One dimensional approximations of neuronal dynamics reveal computational strategy

    Publication

  3. The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks

    Publication

  4. Cell-type-specific population dynamics of diverse reward computations

    Publication
    Computational analysis of cellular activity has developed largely independently of modern transcriptomic cell typology, but integrating these approaches may be essential for full insight into cellular-level mechanisms …

  5. Recurrent Connections in the Primate Ventral Visual Stream Mediate a Trade-Off Between Task Performance and Network Size During Core Object Recognition

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    The computational role of the abundant feedback connections in the ventral visual stream is unclear, enabling humans and nonhuman primates to effortlessly recognize objects across a multitude of …

  6. Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems

    Publication
    Recurrent neural networks (RNNs) are powerful models for processing time-series data, but it remains challenging to understand how they function. Improving this understanding is of substantial interest …

  7. Computation Through Neural Population Dynamics

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    Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of …

  8. Harnessing behavioral diversity to understand neural computations for cognition

    Publication
    With the increasing acquisition of large-scale neural recordings comes the challenge of inferring the computations they perform and understanding how these give rise to behavior. Here, we …

  9. Making brain-machine interfaces robust to future neural variability

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    A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when …