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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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 |
work_keys_str_mv |
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