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Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states
The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4518200/ https://www.ncbi.nlm.nih.gov/pubmed/26283956 http://dx.doi.org/10.3389/fncom.2015.00096 |
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author | Dao Duc, Khanh Parutto, Pierre Chen, Xiaowei Epsztein, Jérôme Konnerth, Arthur Holcman, David |
author_facet | Dao Duc, Khanh Parutto, Pierre Chen, Xiaowei Epsztein, Jérôme Konnerth, Arthur Holcman, David |
author_sort | Dao Duc, Khanh |
collection | PubMed |
description | The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-field model that the residence time of the neuronal membrane potential in cortical Up states does not follow a Poissonian law, but presents several peaks. Furthermore, the present modeling approach allows extracting some information about the neuronal network connectivity from the time distribution histogram. Based on a synaptic-depression model, we find that these peaks, that can be observed in histograms of patch-clamp recordings are not artifacts of electrophysiological measurements, but rather are an inherent property of the network dynamics. Analysis of the equations reveals a stable focus located close to the unstable limit cycle, delimiting a region that defines the Up state. The model further shows that the peaks observed in the Up state time distribution are due to winding around the focus before escaping from the basin of attraction. Finally, we use in vivo recordings of intracellular membrane potential and we recover from the peak distribution, some information about the network connectivity. We conclude that it is possible to recover the network connectivity from the distribution of times that the neuronal membrane voltage spends in Up states. |
format | Online Article Text |
id | pubmed-4518200 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-45182002015-08-17 Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states Dao Duc, Khanh Parutto, Pierre Chen, Xiaowei Epsztein, Jérôme Konnerth, Arthur Holcman, David Front Comput Neurosci Neuroscience The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-field model that the residence time of the neuronal membrane potential in cortical Up states does not follow a Poissonian law, but presents several peaks. Furthermore, the present modeling approach allows extracting some information about the neuronal network connectivity from the time distribution histogram. Based on a synaptic-depression model, we find that these peaks, that can be observed in histograms of patch-clamp recordings are not artifacts of electrophysiological measurements, but rather are an inherent property of the network dynamics. Analysis of the equations reveals a stable focus located close to the unstable limit cycle, delimiting a region that defines the Up state. The model further shows that the peaks observed in the Up state time distribution are due to winding around the focus before escaping from the basin of attraction. Finally, we use in vivo recordings of intracellular membrane potential and we recover from the peak distribution, some information about the network connectivity. We conclude that it is possible to recover the network connectivity from the distribution of times that the neuronal membrane voltage spends in Up states. Frontiers Media S.A. 2015-07-29 /pmc/articles/PMC4518200/ /pubmed/26283956 http://dx.doi.org/10.3389/fncom.2015.00096 Text en Copyright © 2015 Dao Duc, Parutto, Chen, Epsztein, Konnerth and Holcman. 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 and 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 Dao Duc, Khanh Parutto, Pierre Chen, Xiaowei Epsztein, Jérôme Konnerth, Arthur Holcman, David Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states |
title | Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states |
title_full | Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states |
title_fullStr | Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states |
title_full_unstemmed | Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states |
title_short | Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states |
title_sort | synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in up states |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4518200/ https://www.ncbi.nlm.nih.gov/pubmed/26283956 http://dx.doi.org/10.3389/fncom.2015.00096 |
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