Cargando…

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

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–inhibi...

Descripción completa

Detalles Bibliográficos
Autores principales: Echeveste, Rodrigo, Aitchison, Laurence, Hennequin, Guillaume, Lengyel, Máté
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610392/
https://www.ncbi.nlm.nih.gov/pubmed/32778794
http://dx.doi.org/10.1038/s41593-020-0671-1
_version_ 1783605184965902336
author Echeveste, Rodrigo
Aitchison, Laurence
Hennequin, Guillaume
Lengyel, Máté
author_facet Echeveste, Rodrigo
Aitchison, Laurence
Hennequin, Guillaume
Lengyel, Máté
author_sort Echeveste, Rodrigo
collection PubMed
description 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, as well as 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 awake monkey recordings. 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.
format Online
Article
Text
id pubmed-7610392
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-76103922021-03-23 Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference Echeveste, Rodrigo Aitchison, Laurence Hennequin, Guillaume Lengyel, Máté Nat Neurosci Article 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, as well as 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 awake monkey recordings. 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. 2020-09-01 2020-08-10 /pmc/articles/PMC7610392/ /pubmed/32778794 http://dx.doi.org/10.1038/s41593-020-0671-1 Text en http://www.nature.com/authors/editorial_policies/license.html#termsUsers may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Echeveste, Rodrigo
Aitchison, Laurence
Hennequin, Guillaume
Lengyel, Máté
Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
title Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
title_full Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
title_fullStr Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
title_full_unstemmed Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
title_short Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
title_sort cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610392/
https://www.ncbi.nlm.nih.gov/pubmed/32778794
http://dx.doi.org/10.1038/s41593-020-0671-1
work_keys_str_mv AT echevesterodrigo corticallikedynamicsinrecurrentcircuitsoptimizedforsamplingbasedprobabilisticinference
AT aitchisonlaurence corticallikedynamicsinrecurrentcircuitsoptimizedforsamplingbasedprobabilisticinference
AT hennequinguillaume corticallikedynamicsinrecurrentcircuitsoptimizedforsamplingbasedprobabilisticinference
AT lengyelmate corticallikedynamicsinrecurrentcircuitsoptimizedforsamplingbasedprobabilisticinference