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Internally generated population activity in cortical networks hinders information transmission

How neuronal variability affects sensory coding is a central question in systems neuroscience, often with complex and model-dependent answers. Many studies explore population models with a parametric structure for response tuning and variability, preventing an analysis of how synaptic circuitry esta...

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Autores principales: Huang, Chengcheng, Pouget, Alexandre, Doiron, Brent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159697/
https://www.ncbi.nlm.nih.gov/pubmed/35648863
http://dx.doi.org/10.1126/sciadv.abg5244
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author Huang, Chengcheng
Pouget, Alexandre
Doiron, Brent
author_facet Huang, Chengcheng
Pouget, Alexandre
Doiron, Brent
author_sort Huang, Chengcheng
collection PubMed
description How neuronal variability affects sensory coding is a central question in systems neuroscience, often with complex and model-dependent answers. Many studies explore population models with a parametric structure for response tuning and variability, preventing an analysis of how synaptic circuitry establishes neural codes. We study stimulus coding in networks of spiking neuron models with spatially ordered excitatory and inhibitory connectivity. The wiring structure is capable of producing rich population-wide shared neuronal variability that agrees with many features of recorded cortical activity. While both the spatial scales of feedforward and recurrent projections strongly affect noise correlations, only recurrent projections, and in particular inhibitory projections, can introduce correlations that limit the stimulus information available to a decoder. Using a spatial neural field model, we relate the recurrent circuit conditions for information limiting noise correlations to how recurrent excitation and inhibition can form spatiotemporal patterns of population-wide activity.
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spelling pubmed-91596972022-06-16 Internally generated population activity in cortical networks hinders information transmission Huang, Chengcheng Pouget, Alexandre Doiron, Brent Sci Adv Neuroscience How neuronal variability affects sensory coding is a central question in systems neuroscience, often with complex and model-dependent answers. Many studies explore population models with a parametric structure for response tuning and variability, preventing an analysis of how synaptic circuitry establishes neural codes. We study stimulus coding in networks of spiking neuron models with spatially ordered excitatory and inhibitory connectivity. The wiring structure is capable of producing rich population-wide shared neuronal variability that agrees with many features of recorded cortical activity. While both the spatial scales of feedforward and recurrent projections strongly affect noise correlations, only recurrent projections, and in particular inhibitory projections, can introduce correlations that limit the stimulus information available to a decoder. Using a spatial neural field model, we relate the recurrent circuit conditions for information limiting noise correlations to how recurrent excitation and inhibition can form spatiotemporal patterns of population-wide activity. American Association for the Advancement of Science 2022-06-01 /pmc/articles/PMC9159697/ /pubmed/35648863 http://dx.doi.org/10.1126/sciadv.abg5244 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Neuroscience
Huang, Chengcheng
Pouget, Alexandre
Doiron, Brent
Internally generated population activity in cortical networks hinders information transmission
title Internally generated population activity in cortical networks hinders information transmission
title_full Internally generated population activity in cortical networks hinders information transmission
title_fullStr Internally generated population activity in cortical networks hinders information transmission
title_full_unstemmed Internally generated population activity in cortical networks hinders information transmission
title_short Internally generated population activity in cortical networks hinders information transmission
title_sort internally generated population activity in cortical networks hinders information transmission
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159697/
https://www.ncbi.nlm.nih.gov/pubmed/35648863
http://dx.doi.org/10.1126/sciadv.abg5244
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