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

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Aitchison, Laurence 
Hennequin, Guillaume  ORCID logo  https://orcid.org/0000-0002-7296-6870

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.

Description

Keywords

Animals, Haplorhini, Humans, Models, Neurological, Neural Networks, Computer, Visual Cortex, Visual Pathways

Journal Title

Nat Neurosci

Conference Name

Journal ISSN

1097-6256
1546-1726

Volume Title

23

Publisher

Springer Science and Business Media LLC
Sponsorship
Wellcome Trust (095621/Z/11/Z)
Wellcome Trust (202111/Z/16/Z)
Wellcome Trust (212262/Z/18/Z)