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...

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Main Authors: Khanh eDao Duc, Pierre eParutto, Xiaowei eChen, Jérôme eEpsztein, Arthur eKonnerth, David eHolcman
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-07-01
Series:Frontiers in Computational Neuroscience
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00096/full
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spelling doaj-0ef7bc1e58fa4b27bf3f9f8d6f19e92c2020-11-24T22:16:39ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882015-07-01910.3389/fncom.2015.00096141278Synaptic Dynamics and Neuronal Network Connectivity are reflected in the Distribution of Times in Up statesKhanh eDao Duc0Pierre eParutto1Xiaowei eChen2Jérôme eEpsztein3Arthur eKonnerth4David eHolcman5Ecole NormaleEcole NormaleTechnische Universität MünchenInstitut de Neurobiologie de la Méditerranée INMEDTechnische Universität MünchenEcole NormaleThe 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 times 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.http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00096/fullNoisemodelingneuronal networksinverse problemsynaptic depressionup-states
collection DOAJ
language English
format Article
sources DOAJ
author Khanh eDao Duc
Pierre eParutto
Xiaowei eChen
Jérôme eEpsztein
Arthur eKonnerth
David eHolcman
spellingShingle Khanh eDao Duc
Pierre eParutto
Xiaowei eChen
Jérôme eEpsztein
Arthur eKonnerth
David eHolcman
Synaptic Dynamics and Neuronal Network Connectivity are reflected in the Distribution of Times in Up states
Frontiers in Computational Neuroscience
Noise
modeling
neuronal networks
inverse problem
synaptic depression
up-states
author_facet Khanh eDao Duc
Pierre eParutto
Xiaowei eChen
Jérôme eEpsztein
Arthur eKonnerth
David eHolcman
author_sort Khanh eDao Duc
title 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_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_sort synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in up states
publisher Frontiers Media S.A.
series Frontiers in Computational Neuroscience
issn 1662-5188
publishDate 2015-07-01
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 times 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.
topic Noise
modeling
neuronal networks
inverse problem
synaptic depression
up-states
url http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00096/full
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AT jeromeeepsztein synapticdynamicsandneuronalnetworkconnectivityarereflectedinthedistributionoftimesinupstates
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