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Complexity Collapse, Fluctuating Synchrony, and Transient Chaos in Neural Networks With Delay Clusters
Neural circuits operate with delays over a range of time scales, from a few milliseconds in recurrent local circuitry to tens of milliseconds or more for communication between populations. Modeling usually incorporates single fixed delays, meant to represent the mean conduction delay between neurons...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8639886/ https://www.ncbi.nlm.nih.gov/pubmed/34867219 http://dx.doi.org/10.3389/fnsys.2021.720744 |
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author | Tavakoli, S. Kamyar Longtin, André |
author_facet | Tavakoli, S. Kamyar Longtin, André |
author_sort | Tavakoli, S. Kamyar |
collection | PubMed |
description | Neural circuits operate with delays over a range of time scales, from a few milliseconds in recurrent local circuitry to tens of milliseconds or more for communication between populations. Modeling usually incorporates single fixed delays, meant to represent the mean conduction delay between neurons making up the circuit. We explore conditions under which the inclusion of more delays in a high-dimensional chaotic neural network leads to a reduction in dynamical complexity, a phenomenon recently described as multi-delay complexity collapse (CC) in delay-differential equations with one to three variables. We consider a recurrent local network of 80% excitatory and 20% inhibitory rate model neurons with 10% connection probability. An increase in the width of the distribution of local delays, even to unrealistically large values, does not cause CC, nor does adding more local delays. Interestingly, multiple small local delays can cause CC provided there is a moderate global delayed inhibitory feedback and random initial conditions. CC then occurs through the settling of transient chaos onto a limit cycle. In this regime, there is a form of noise-induced order in which the mean activity variance decreases as the noise increases and disrupts the synchrony. Another novel form of CC is seen where global delayed feedback causes “dropouts,” i.e., epochs of low firing rate network synchrony. Their alternation with epochs of higher firing rate asynchrony closely follows Poisson statistics. Such dropouts are promoted by larger global feedback strength and delay. Finally, periodic driving of the chaotic regime with global feedback can cause CC; the extinction of chaos can outlast the forcing, sometimes permanently. Our results suggest a wealth of phenomena that remain to be discovered in networks with clusters of delays. |
format | Online Article Text |
id | pubmed-8639886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86398862021-12-04 Complexity Collapse, Fluctuating Synchrony, and Transient Chaos in Neural Networks With Delay Clusters Tavakoli, S. Kamyar Longtin, André Front Syst Neurosci Neuroscience Neural circuits operate with delays over a range of time scales, from a few milliseconds in recurrent local circuitry to tens of milliseconds or more for communication between populations. Modeling usually incorporates single fixed delays, meant to represent the mean conduction delay between neurons making up the circuit. We explore conditions under which the inclusion of more delays in a high-dimensional chaotic neural network leads to a reduction in dynamical complexity, a phenomenon recently described as multi-delay complexity collapse (CC) in delay-differential equations with one to three variables. We consider a recurrent local network of 80% excitatory and 20% inhibitory rate model neurons with 10% connection probability. An increase in the width of the distribution of local delays, even to unrealistically large values, does not cause CC, nor does adding more local delays. Interestingly, multiple small local delays can cause CC provided there is a moderate global delayed inhibitory feedback and random initial conditions. CC then occurs through the settling of transient chaos onto a limit cycle. In this regime, there is a form of noise-induced order in which the mean activity variance decreases as the noise increases and disrupts the synchrony. Another novel form of CC is seen where global delayed feedback causes “dropouts,” i.e., epochs of low firing rate network synchrony. Their alternation with epochs of higher firing rate asynchrony closely follows Poisson statistics. Such dropouts are promoted by larger global feedback strength and delay. Finally, periodic driving of the chaotic regime with global feedback can cause CC; the extinction of chaos can outlast the forcing, sometimes permanently. Our results suggest a wealth of phenomena that remain to be discovered in networks with clusters of delays. Frontiers Media S.A. 2021-11-19 /pmc/articles/PMC8639886/ /pubmed/34867219 http://dx.doi.org/10.3389/fnsys.2021.720744 Text en Copyright © 2021 Tavakoli and Longtin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Tavakoli, S. Kamyar Longtin, André Complexity Collapse, Fluctuating Synchrony, and Transient Chaos in Neural Networks With Delay Clusters |
title | Complexity Collapse, Fluctuating Synchrony, and Transient Chaos in Neural Networks With Delay Clusters |
title_full | Complexity Collapse, Fluctuating Synchrony, and Transient Chaos in Neural Networks With Delay Clusters |
title_fullStr | Complexity Collapse, Fluctuating Synchrony, and Transient Chaos in Neural Networks With Delay Clusters |
title_full_unstemmed | Complexity Collapse, Fluctuating Synchrony, and Transient Chaos in Neural Networks With Delay Clusters |
title_short | Complexity Collapse, Fluctuating Synchrony, and Transient Chaos in Neural Networks With Delay Clusters |
title_sort | complexity collapse, fluctuating synchrony, and transient chaos in neural networks with delay clusters |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8639886/ https://www.ncbi.nlm.nih.gov/pubmed/34867219 http://dx.doi.org/10.3389/fnsys.2021.720744 |
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