The role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neurons

Neuronal network models often assume a fixed probability of connectionbetween neurons. This assumption leads to random networks withbinomial in-degree and out-degree distributions which are relatively narrow. Here I study the effect of broaddegree distributions on network dynamics by interpolati...

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Main Author: Alex eRoxin
Format: Article
Language:English
Published: Frontiers Media S.A. 2011-03-01
Series:Frontiers in Computational Neuroscience
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00008/full
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spelling doaj-2a1d6dad7a7045c283f76e5bd2d0696b2020-11-24T23:56:02ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882011-03-01510.3389/fncom.2011.000088937The role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neuronsAlex eRoxin0Institut d'Investigacions Biomèdiques August Pi SunyerNeuronal network models often assume a fixed probability of connectionbetween neurons. This assumption leads to random networks withbinomial in-degree and out-degree distributions which are relatively narrow. Here I study the effect of broaddegree distributions on network dynamics by interpolating between abinomial and a truncated powerlaw distribution for the in-degree andout-degree independently. This is done both for an inhibitory network(I network) as well as for the recurrent excitatory connections in anetwork of excitatory and inhibitory neurons (EI network). In bothcases increasing the width of the in-degree distribution affects theglobal state of the network by driving transitions betweenasynchronous behavior and oscillations. This effect is reproduced ina simplified rate model which includes the heterogeneity in neuronalinput due to the in-degree of cells. On the other hand, broadeningthe out-degree distribution is shown to increase the fraction ofcommon inputs to pairs of neurons. This leads to increases in theamplitude of the cross-correlation (CC) of synaptic currents. In thecase of the I network, despite strong oscillatory CCs in the currents, CCs of the membrane potential are low due to filtering and reset effects, leading to very weak CCs of the spikecount. In the asynchronous regime ofthe EI network, broadening the out-degree increases the amplitude ofCCs in the recurrent excitatory currents, while CC of the totalcurrent is essentially unaffected as are pairwise spikingcorrelations. This is due to a dynamic balance between excitatoryand inhibitory synaptic currents. In the oscillatory regime, changesin the out-degree can have a large effect on spiking correlations andeven on the qualitative dynamical state of the network.http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00008/fulloscillationsnetwork connectivityheterogeneityNeuronal dynamicsdegree distributionpairwise correlations
collection DOAJ
language English
format Article
sources DOAJ
author Alex eRoxin
spellingShingle Alex eRoxin
The role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neurons
Frontiers in Computational Neuroscience
oscillations
network connectivity
heterogeneity
Neuronal dynamics
degree distribution
pairwise correlations
author_facet Alex eRoxin
author_sort Alex eRoxin
title The role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neurons
title_short The role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neurons
title_full The role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neurons
title_fullStr The role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neurons
title_full_unstemmed The role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neurons
title_sort role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neurons
publisher Frontiers Media S.A.
series Frontiers in Computational Neuroscience
issn 1662-5188
publishDate 2011-03-01
description Neuronal network models often assume a fixed probability of connectionbetween neurons. This assumption leads to random networks withbinomial in-degree and out-degree distributions which are relatively narrow. Here I study the effect of broaddegree distributions on network dynamics by interpolating between abinomial and a truncated powerlaw distribution for the in-degree andout-degree independently. This is done both for an inhibitory network(I network) as well as for the recurrent excitatory connections in anetwork of excitatory and inhibitory neurons (EI network). In bothcases increasing the width of the in-degree distribution affects theglobal state of the network by driving transitions betweenasynchronous behavior and oscillations. This effect is reproduced ina simplified rate model which includes the heterogeneity in neuronalinput due to the in-degree of cells. On the other hand, broadeningthe out-degree distribution is shown to increase the fraction ofcommon inputs to pairs of neurons. This leads to increases in theamplitude of the cross-correlation (CC) of synaptic currents. In thecase of the I network, despite strong oscillatory CCs in the currents, CCs of the membrane potential are low due to filtering and reset effects, leading to very weak CCs of the spikecount. In the asynchronous regime ofthe EI network, broadening the out-degree increases the amplitude ofCCs in the recurrent excitatory currents, while CC of the totalcurrent is essentially unaffected as are pairwise spikingcorrelations. This is due to a dynamic balance between excitatoryand inhibitory synaptic currents. In the oscillatory regime, changesin the out-degree can have a large effect on spiking correlations andeven on the qualitative dynamical state of the network.
topic oscillations
network connectivity
heterogeneity
Neuronal dynamics
degree distribution
pairwise correlations
url http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00008/full
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