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Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex
Correlated activity fluctuations in the neocortex influence sensory responses and behavior. Neural correlations reflect anatomical connectivity but also change dynamically with cognitive states such as attention. Yet, the network mechanisms defining the population structure of correlations remain un...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8748999/ https://www.ncbi.nlm.nih.gov/pubmed/35013259 http://dx.doi.org/10.1038/s41467-021-27724-4 |
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author | Shi, Yan-Liang Steinmetz, Nicholas A. Moore, Tirin Boahen, Kwabena Engel, Tatiana A. |
author_facet | Shi, Yan-Liang Steinmetz, Nicholas A. Moore, Tirin Boahen, Kwabena Engel, Tatiana A. |
author_sort | Shi, Yan-Liang |
collection | PubMed |
description | Correlated activity fluctuations in the neocortex influence sensory responses and behavior. Neural correlations reflect anatomical connectivity but also change dynamically with cognitive states such as attention. Yet, the network mechanisms defining the population structure of correlations remain unknown. We measured correlations within columns in the visual cortex. We show that the magnitude of correlations, their attentional modulation, and dependence on lateral distance are explained by columnar On-Off dynamics, which are synchronous activity fluctuations reflecting cortical state. We developed a network model in which the On-Off dynamics propagate across nearby columns generating spatial correlations with the extent controlled by attentional inputs. This mechanism, unlike previous proposals, predicts spatially non-uniform changes in correlations during attention. We confirm this prediction in our columnar recordings by showing that in superficial layers the largest changes in correlations occur at intermediate lateral distances. Our results reveal how spatially structured patterns of correlated variability emerge through interactions of cortical state dynamics, anatomical connectivity, and attention. |
format | Online Article Text |
id | pubmed-8748999 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87489992022-01-20 Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex Shi, Yan-Liang Steinmetz, Nicholas A. Moore, Tirin Boahen, Kwabena Engel, Tatiana A. Nat Commun Article Correlated activity fluctuations in the neocortex influence sensory responses and behavior. Neural correlations reflect anatomical connectivity but also change dynamically with cognitive states such as attention. Yet, the network mechanisms defining the population structure of correlations remain unknown. We measured correlations within columns in the visual cortex. We show that the magnitude of correlations, their attentional modulation, and dependence on lateral distance are explained by columnar On-Off dynamics, which are synchronous activity fluctuations reflecting cortical state. We developed a network model in which the On-Off dynamics propagate across nearby columns generating spatial correlations with the extent controlled by attentional inputs. This mechanism, unlike previous proposals, predicts spatially non-uniform changes in correlations during attention. We confirm this prediction in our columnar recordings by showing that in superficial layers the largest changes in correlations occur at intermediate lateral distances. Our results reveal how spatially structured patterns of correlated variability emerge through interactions of cortical state dynamics, anatomical connectivity, and attention. Nature Publishing Group UK 2022-01-10 /pmc/articles/PMC8748999/ /pubmed/35013259 http://dx.doi.org/10.1038/s41467-021-27724-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Shi, Yan-Liang Steinmetz, Nicholas A. Moore, Tirin Boahen, Kwabena Engel, Tatiana A. Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex |
title | Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex |
title_full | Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex |
title_fullStr | Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex |
title_full_unstemmed | Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex |
title_short | Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex |
title_sort | cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8748999/ https://www.ncbi.nlm.nih.gov/pubmed/35013259 http://dx.doi.org/10.1038/s41467-021-27724-4 |
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