How structure determines correlations in neuronal networks.

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the n...

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Autores principales:	Volker Pernice, Benjamin Staude, Stefano Cardanobile, Stefan Rotter
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/002ecf1edc56403d9fb516e2263b5a7e
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spelling	oai:doaj.org-article:002ecf1edc56403d9fb516e2263b5a7e2021-11-18T05:50:31ZHow structure determines correlations in neuronal networks.1553-734X1553-735810.1371/journal.pcbi.1002059https://doaj.org/article/002ecf1edc56403d9fb516e2263b5a7e2011-05-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21625580/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.Volker PerniceBenjamin StaudeStefano CardanobileStefan RotterPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 5, p e1002059 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Volker Pernice Benjamin Staude Stefano Cardanobile Stefan Rotter How structure determines correlations in neuronal networks.
description	Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.
format	article
author	Volker Pernice Benjamin Staude Stefano Cardanobile Stefan Rotter
author_facet	Volker Pernice Benjamin Staude Stefano Cardanobile Stefan Rotter
author_sort	Volker Pernice
title	How structure determines correlations in neuronal networks.
title_short	How structure determines correlations in neuronal networks.
title_full	How structure determines correlations in neuronal networks.
title_fullStr	How structure determines correlations in neuronal networks.
title_full_unstemmed	How structure determines correlations in neuronal networks.
title_sort	how structure determines correlations in neuronal networks.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/002ecf1edc56403d9fb516e2263b5a7e
work_keys_str_mv	AT volkerpernice howstructuredeterminescorrelationsinneuronalnetworks AT benjaminstaude howstructuredeterminescorrelationsinneuronalnetworks AT stefanocardanobile howstructuredeterminescorrelationsinneuronalnetworks AT stefanrotter howstructuredeterminescorrelationsinneuronalnetworks
_version_	1718424768196116480

How structure determines correlations in neuronal networks.

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