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Rhythmic inhibition allows neural networks to search for maximally consistent states


Mostafa, Hesham; Muller, Lorenz K; Indiveri, Giacomo (2015). Rhythmic inhibition allows neural networks to search for maximally consistent states. Neural Computation, 27(12):2510-2547.

Abstract

Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuits, yet its computational role remains elusive. We show that a model of gamma-band rhythmic inhibition allows networks of coupled cortical circuit motifs to search for network configurations that best reconcile external inputs with an internal consistency model encoded in the network connectivity. We show that Hebbian plasticity allows the networks to learn the consistency model by example. The search dynamics driven by rhythmic inhibition enable the described networks to solve difficult constraint satisfaction problems without making assumptions about the form of stochastic fluctuations in the network. We show that the search dynamics are well approximated by a stochastic sampling process. We use the described networks to reproduce perceptual multistability phenomena with switching times that are a good match to experimental data and show that they provide a general neural framework that can be used to model other perceptual inference phenomena.

Abstract

Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuits, yet its computational role remains elusive. We show that a model of gamma-band rhythmic inhibition allows networks of coupled cortical circuit motifs to search for network configurations that best reconcile external inputs with an internal consistency model encoded in the network connectivity. We show that Hebbian plasticity allows the networks to learn the consistency model by example. The search dynamics driven by rhythmic inhibition enable the described networks to solve difficult constraint satisfaction problems without making assumptions about the form of stochastic fluctuations in the network. We show that the search dynamics are well approximated by a stochastic sampling process. We use the described networks to reproduce perceptual multistability phenomena with switching times that are a good match to experimental data and show that they provide a general neural framework that can be used to model other perceptual inference phenomena.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2015
Deposited On:22 Feb 2016 13:21
Last Modified:05 Apr 2016 20:04
Publisher:MIT Press
Series Name:Neural Computation
Number of Pages:38
ISSN:0899-7667
Publisher DOI:https://doi.org/10.1162/NECO_a_00785

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