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Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons
The study of balanced networks of excitatory and inhibitory neurons has led to several open questions. On the one hand it is yet unclear whether the asynchronous state observed in the brain is autonomously generated, or if it results from the interplay between external drivings and internal dynamics...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030235/ https://www.ncbi.nlm.nih.gov/pubmed/27708572 http://dx.doi.org/10.3389/fncom.2016.00098 |
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author | Echeveste, Rodrigo Gros, Claudius |
author_facet | Echeveste, Rodrigo Gros, Claudius |
author_sort | Echeveste, Rodrigo |
collection | PubMed |
description | The study of balanced networks of excitatory and inhibitory neurons has led to several open questions. On the one hand it is yet unclear whether the asynchronous state observed in the brain is autonomously generated, or if it results from the interplay between external drivings and internal dynamics. It is also not known, which kind of network variabilities will lead to irregular spiking and which to synchronous firing states. Here we show how isolated networks of purely excitatory neurons generically show asynchronous firing whenever a minimal level of structural variability is present together with a refractory period. Our autonomous networks are composed of excitable units, in the form of leaky integrators spiking only in response to driving currents, remaining otherwise quiet. For a non-uniform network, composed exclusively of excitatory neurons, we find a rich repertoire of self-induced dynamical states. We show in particular that asynchronous drifting states may be stabilized in purely excitatory networks whenever a refractory period is present. Other states found are either fully synchronized or mixed, containing both drifting and synchronized components. The individual neurons considered are excitable and hence do not dispose of intrinsic natural firing frequencies. An effective network-wide distribution of natural frequencies is however generated autonomously through self-consistent feedback loops. The asynchronous drifting state is, additionally, amenable to an analytic solution. We find two types of asynchronous activity, with the individual neurons spiking regularly in the pure drifting state, albeit with a continuous distribution of firing frequencies. The activity of the drifting component, however, becomes irregular in the mixed state, due to the periodic driving of the synchronized component. We propose a new tool for the study of chaos in spiking neural networks, which consists of an analysis of the time series of pairs of consecutive interspike intervals. In this space, we show that a strange attractor with a fractal dimension of about 1.8 is formed in the mentioned mixed state. |
format | Online Article Text |
id | pubmed-5030235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-50302352016-10-05 Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons Echeveste, Rodrigo Gros, Claudius Front Comput Neurosci Neuroscience The study of balanced networks of excitatory and inhibitory neurons has led to several open questions. On the one hand it is yet unclear whether the asynchronous state observed in the brain is autonomously generated, or if it results from the interplay between external drivings and internal dynamics. It is also not known, which kind of network variabilities will lead to irregular spiking and which to synchronous firing states. Here we show how isolated networks of purely excitatory neurons generically show asynchronous firing whenever a minimal level of structural variability is present together with a refractory period. Our autonomous networks are composed of excitable units, in the form of leaky integrators spiking only in response to driving currents, remaining otherwise quiet. For a non-uniform network, composed exclusively of excitatory neurons, we find a rich repertoire of self-induced dynamical states. We show in particular that asynchronous drifting states may be stabilized in purely excitatory networks whenever a refractory period is present. Other states found are either fully synchronized or mixed, containing both drifting and synchronized components. The individual neurons considered are excitable and hence do not dispose of intrinsic natural firing frequencies. An effective network-wide distribution of natural frequencies is however generated autonomously through self-consistent feedback loops. The asynchronous drifting state is, additionally, amenable to an analytic solution. We find two types of asynchronous activity, with the individual neurons spiking regularly in the pure drifting state, albeit with a continuous distribution of firing frequencies. The activity of the drifting component, however, becomes irregular in the mixed state, due to the periodic driving of the synchronized component. We propose a new tool for the study of chaos in spiking neural networks, which consists of an analysis of the time series of pairs of consecutive interspike intervals. In this space, we show that a strange attractor with a fractal dimension of about 1.8 is formed in the mentioned mixed state. Frontiers Media S.A. 2016-09-21 /pmc/articles/PMC5030235/ /pubmed/27708572 http://dx.doi.org/10.3389/fncom.2016.00098 Text en Copyright © 2016 Echeveste and Gros. http://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) or licensor 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 Echeveste, Rodrigo Gros, Claudius Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons |
title | Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons |
title_full | Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons |
title_fullStr | Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons |
title_full_unstemmed | Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons |
title_short | Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons |
title_sort | drifting states and synchronization induced chaos in autonomous networks of excitable neurons |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030235/ https://www.ncbi.nlm.nih.gov/pubmed/27708572 http://dx.doi.org/10.3389/fncom.2016.00098 |
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