Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference.

Journal: Nature neuroscience
PMID: 32778794

Abstract

Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory-inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function-fast sampling-based inference-and predict further properties of these motifs that can be tested in future experiments.

Authors

  • Rodrigo Echeveste
    Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK. recheveste@sinc.unl.edu.ar.
  • Laurence Aitchison
    Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
  • Guillaume Hennequin
    Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
  • Máté Lengyel
    Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom, and Department of Cognitive Science, Central European University, Budapest 1051, Hungary.

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