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  1. Evolution of neural activity in circuits bridging sensory and abstract knowledge

    Publication

  2. Developmental and evolutionary constraints on olfactory circuit selection

    Publication
    SignificanceIn this work, we explore the hypothesis that biological neural networks optimize their architecture, through evolution, for learning. We study early olfactory circuits of mammals and insects, which …

  3. Sparse connectivity for MAP inference in linear models using sister mitral cells

    Publication
    Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in studies of sensory processing is …

  4. Synaptic plasticity as Bayesian inference

    Publication
    of synaptic weights must be set based on noisy, often ambiguous, sensory information. In such a high-noise regime, keeping track of probability distributions over weights is the …

  5. Synaptic plasticity as Bayesian inference

    Publication
    Learning, especially rapid learning, is critical for survival. However, learning is hard; a large number of synaptic weights must be set based on noisy, often ambiguous, sensory information …

  6. Rapid Bayesian learning in the mammalian olfactory system

    Publication
    Many experimental studies suggest that animals can rapidly learn to identify odors and predict the rewards associated with them. However, the underlying plasticity mechanism remains elusive. In particular …

  7. Noisy Synaptic Conductance: Bug or a Feature?

    Publication
    More often than not, action potentials fail to trigger neurotransmitter release. And even when neurotransmitter is released, the resulting change in synaptic conductance is highly variable. Given the …

  8. Excitatory and Inhibitory Subnetworks Are Equally Selective during Decision-Making and Emerge Simultaneously during Learning.

    Publication
    Inhibitory neurons, which play a critical role in decision-making models, are often simplified as a single pool of non-selective neurons lacking connection specificity. This assumption is …

  9. A deep learning framework for neuroscience.

    Publication
    Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on …