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Structure-Dynamics Relationships in Bursting Neuronal Networks Revealed Using a Prediction Framework

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Structure-Dynamics Relationships in Bursting Neuronal Networks Revealed Using a Prediction Framework. / Mäki-Marttunen, Tuomo; Acimovic, Jugoslava; Linne, Marja-Leena; Ruohonen, Keijo.

In: PLoS ONE, Vol. 8, No. 7, e69373, 2013, p. 1-16.

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@article{b8cf60968986400ba773739e6b8d3ecb,
title = "Structure-Dynamics Relationships in Bursting Neuronal Networks Revealed Using a Prediction Framework",
abstract = "The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small (N~100) networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger (N~900) networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences.",
author = "Tuomo M{\"a}ki-Marttunen and Jugoslava Acimovic and Marja-Leena Linne and Keijo Ruohonen",
note = "Contribution: organisation=sgn,FACT1=0.5<br/>Contribution: organisation=mat,FACT2=0.5<br/>Portfolio EDEND: 2013-12-29<br/>Publisher name: Public Library of Science",
year = "2013",
doi = "10.1371/journal.pone.0069373",
language = "English",
volume = "8",
pages = "1--16",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "7",

}

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TY - JOUR

T1 - Structure-Dynamics Relationships in Bursting Neuronal Networks Revealed Using a Prediction Framework

AU - Mäki-Marttunen, Tuomo

AU - Acimovic, Jugoslava

AU - Linne, Marja-Leena

AU - Ruohonen, Keijo

N1 - Contribution: organisation=sgn,FACT1=0.5<br/>Contribution: organisation=mat,FACT2=0.5<br/>Portfolio EDEND: 2013-12-29<br/>Publisher name: Public Library of Science

PY - 2013

Y1 - 2013

N2 - The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small (N~100) networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger (N~900) networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences.

AB - The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small (N~100) networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger (N~900) networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences.

U2 - 10.1371/journal.pone.0069373

DO - 10.1371/journal.pone.0069373

M3 - Article

VL - 8

SP - 1

EP - 16

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 7

M1 - e69373

ER -