Measuring symmetry, asymmetry and randomness in neural network connectivity.

Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-s...

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Autores principales:	Umberto Esposito, Michele Giugliano, Mark van Rossum, Eleni Vasilaki
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8cc0eebff1e74487b7cef61b53bbfdca
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spelling	oai:doaj.org-article:8cc0eebff1e74487b7cef61b53bbfdca2021-11-25T06:09:15ZMeasuring symmetry, asymmetry and randomness in neural network connectivity.1932-620310.1371/journal.pone.0100805https://doaj.org/article/8cc0eebff1e74487b7cef61b53bbfdca2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25006663/?tool=EBIhttps://doaj.org/toc/1932-6203Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-symmetric motifs, i.e. within a pair of neurons only one neuron projects to the other. We hypothesise that such motifs have been shaped via activity dependent synaptic plasticity processes. As a consequence, learning moves the distribution of the synaptic connections away from randomness. Our aim is to provide a global, macroscopic, single parameter characterisation of the statistical occurrence of bidirectional and unidirectional motifs. To this end we define a symmetry measure that does not require any a priori thresholding of the weights or knowledge of their maximal value. We calculate its mean and variance for random uniform or Gaussian distributions, which allows us to introduce a confidence measure of how significantly symmetric or asymmetric a specific configuration is, i.e. how likely it is that the configuration is the result of chance. We demonstrate the discriminatory power of our symmetry measure by inspecting the eigenvalues of different types of connectivity matrices. We show that a Gaussian weight distribution biases the connectivity motifs to more symmetric configurations than a uniform distribution and that introducing a random synaptic pruning, mimicking developmental regulation in synaptogenesis, biases the connectivity motifs to more asymmetric configurations, regardless of the distribution. We expect that our work will benefit the computational modelling community, by providing a systematic way to characterise symmetry and asymmetry in network structures. Further, our symmetry measure will be of use to electrophysiologists that investigate symmetry of network connectivity.Umberto EspositoMichele GiuglianoMark van RossumEleni VasilakiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e100805 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Umberto Esposito Michele Giugliano Mark van Rossum Eleni Vasilaki Measuring symmetry, asymmetry and randomness in neural network connectivity.
description	Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-symmetric motifs, i.e. within a pair of neurons only one neuron projects to the other. We hypothesise that such motifs have been shaped via activity dependent synaptic plasticity processes. As a consequence, learning moves the distribution of the synaptic connections away from randomness. Our aim is to provide a global, macroscopic, single parameter characterisation of the statistical occurrence of bidirectional and unidirectional motifs. To this end we define a symmetry measure that does not require any a priori thresholding of the weights or knowledge of their maximal value. We calculate its mean and variance for random uniform or Gaussian distributions, which allows us to introduce a confidence measure of how significantly symmetric or asymmetric a specific configuration is, i.e. how likely it is that the configuration is the result of chance. We demonstrate the discriminatory power of our symmetry measure by inspecting the eigenvalues of different types of connectivity matrices. We show that a Gaussian weight distribution biases the connectivity motifs to more symmetric configurations than a uniform distribution and that introducing a random synaptic pruning, mimicking developmental regulation in synaptogenesis, biases the connectivity motifs to more asymmetric configurations, regardless of the distribution. We expect that our work will benefit the computational modelling community, by providing a systematic way to characterise symmetry and asymmetry in network structures. Further, our symmetry measure will be of use to electrophysiologists that investigate symmetry of network connectivity.
format	article
author	Umberto Esposito Michele Giugliano Mark van Rossum Eleni Vasilaki
author_facet	Umberto Esposito Michele Giugliano Mark van Rossum Eleni Vasilaki
author_sort	Umberto Esposito
title	Measuring symmetry, asymmetry and randomness in neural network connectivity.
title_short	Measuring symmetry, asymmetry and randomness in neural network connectivity.
title_full	Measuring symmetry, asymmetry and randomness in neural network connectivity.
title_fullStr	Measuring symmetry, asymmetry and randomness in neural network connectivity.
title_full_unstemmed	Measuring symmetry, asymmetry and randomness in neural network connectivity.
title_sort	measuring symmetry, asymmetry and randomness in neural network connectivity.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/8cc0eebff1e74487b7cef61b53bbfdca
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Measuring symmetry, asymmetry and randomness in neural network connectivity.

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