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Dense Neuron Clustering Explains Connectivity Statistics in Cortical Microcircuits
Local cortical circuits appear highly non-random, but the underlying connectivity rule remains elusive. Here, we analyze experimental data observed in layer 5 of rat neocortex and suggest a model for connectivity from which emerge essential observed non-random features of both wiring and weighting....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3986068/ https://www.ncbi.nlm.nih.gov/pubmed/24732632 http://dx.doi.org/10.1371/journal.pone.0094292 |
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author | Klinshov, Vladimir V. Teramae, Jun-nosuke Nekorkin, Vladimir I. Fukai, Tomoki |
author_facet | Klinshov, Vladimir V. Teramae, Jun-nosuke Nekorkin, Vladimir I. Fukai, Tomoki |
author_sort | Klinshov, Vladimir V. |
collection | PubMed |
description | Local cortical circuits appear highly non-random, but the underlying connectivity rule remains elusive. Here, we analyze experimental data observed in layer 5 of rat neocortex and suggest a model for connectivity from which emerge essential observed non-random features of both wiring and weighting. These features include lognormal distributions of synaptic connection strength, anatomical clustering, and strong correlations between clustering and connection strength. Our model predicts that cortical microcircuits contain large groups of densely connected neurons which we call clusters. We show that such a cluster contains about one fifth of all excitatory neurons of a circuit which are very densely connected with stronger than average synapses. We demonstrate that such clustering plays an important role in the network dynamics, namely, it creates bistable neural spiking in small cortical circuits. Furthermore, introducing local clustering in large-scale networks leads to the emergence of various patterns of persistent local activity in an ongoing network activity. Thus, our results may bridge a gap between anatomical structure and persistent activity observed during working memory and other cognitive processes. |
format | Online Article Text |
id | pubmed-3986068 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39860682014-04-15 Dense Neuron Clustering Explains Connectivity Statistics in Cortical Microcircuits Klinshov, Vladimir V. Teramae, Jun-nosuke Nekorkin, Vladimir I. Fukai, Tomoki PLoS One Research Article Local cortical circuits appear highly non-random, but the underlying connectivity rule remains elusive. Here, we analyze experimental data observed in layer 5 of rat neocortex and suggest a model for connectivity from which emerge essential observed non-random features of both wiring and weighting. These features include lognormal distributions of synaptic connection strength, anatomical clustering, and strong correlations between clustering and connection strength. Our model predicts that cortical microcircuits contain large groups of densely connected neurons which we call clusters. We show that such a cluster contains about one fifth of all excitatory neurons of a circuit which are very densely connected with stronger than average synapses. We demonstrate that such clustering plays an important role in the network dynamics, namely, it creates bistable neural spiking in small cortical circuits. Furthermore, introducing local clustering in large-scale networks leads to the emergence of various patterns of persistent local activity in an ongoing network activity. Thus, our results may bridge a gap between anatomical structure and persistent activity observed during working memory and other cognitive processes. Public Library of Science 2014-04-14 /pmc/articles/PMC3986068/ /pubmed/24732632 http://dx.doi.org/10.1371/journal.pone.0094292 Text en © 2014 Klinshov et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Klinshov, Vladimir V. Teramae, Jun-nosuke Nekorkin, Vladimir I. Fukai, Tomoki Dense Neuron Clustering Explains Connectivity Statistics in Cortical Microcircuits |
title | Dense Neuron Clustering Explains Connectivity Statistics in Cortical Microcircuits |
title_full | Dense Neuron Clustering Explains Connectivity Statistics in Cortical Microcircuits |
title_fullStr | Dense Neuron Clustering Explains Connectivity Statistics in Cortical Microcircuits |
title_full_unstemmed | Dense Neuron Clustering Explains Connectivity Statistics in Cortical Microcircuits |
title_short | Dense Neuron Clustering Explains Connectivity Statistics in Cortical Microcircuits |
title_sort | dense neuron clustering explains connectivity statistics in cortical microcircuits |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3986068/ https://www.ncbi.nlm.nih.gov/pubmed/24732632 http://dx.doi.org/10.1371/journal.pone.0094292 |
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