Probing the structure–function relationship with neural networks constructed by solving a system of linear equations

Abstract Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, beha...

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Autores principales:	Camilo J. Mininni, B. Silvano Zanutto
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a33fe0176377443ea1778115dd67343f
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spelling	oai:doaj.org-article:a33fe0176377443ea1778115dd67343f2021-12-02T10:54:06ZProbing the structure–function relationship with neural networks constructed by solving a system of linear equations10.1038/s41598-021-82964-02045-2322https://doaj.org/article/a33fe0176377443ea1778115dd67343f2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82964-0https://doaj.org/toc/2045-2322Abstract Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural, and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate its performance and improve fitting. Here we propose to invert the fitting process by proceeding from the network dynamics towards network parameters. Firing state transitions are chosen according to the transition graph associated with the solution of a task. Then, a system of linear equations is constructed from the network firing states and membrane potentials, in a way that guarantees the consistency of the system. This allows us to uncouple the dynamical features of the model, like its neurons firing rate and correlation, from the structural features, and the task-solving algorithm implemented by the network. We employed our method to probe the structure–function relationship in a sequence memory task. The networks obtained showed connectivity and firing statistics that recapitulated experimental observations. We argue that the proposed method is a complementary and needed alternative to the way neural networks are constructed to model brain function.Camilo J. MininniB. Silvano ZanuttoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Camilo J. Mininni B. Silvano Zanutto Probing the structure–function relationship with neural networks constructed by solving a system of linear equations
description	Abstract Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural, and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate its performance and improve fitting. Here we propose to invert the fitting process by proceeding from the network dynamics towards network parameters. Firing state transitions are chosen according to the transition graph associated with the solution of a task. Then, a system of linear equations is constructed from the network firing states and membrane potentials, in a way that guarantees the consistency of the system. This allows us to uncouple the dynamical features of the model, like its neurons firing rate and correlation, from the structural features, and the task-solving algorithm implemented by the network. We employed our method to probe the structure–function relationship in a sequence memory task. The networks obtained showed connectivity and firing statistics that recapitulated experimental observations. We argue that the proposed method is a complementary and needed alternative to the way neural networks are constructed to model brain function.
format	article
author	Camilo J. Mininni B. Silvano Zanutto
author_facet	Camilo J. Mininni B. Silvano Zanutto
author_sort	Camilo J. Mininni
title	Probing the structure–function relationship with neural networks constructed by solving a system of linear equations
title_short	Probing the structure–function relationship with neural networks constructed by solving a system of linear equations
title_full	Probing the structure–function relationship with neural networks constructed by solving a system of linear equations
title_fullStr	Probing the structure–function relationship with neural networks constructed by solving a system of linear equations
title_full_unstemmed	Probing the structure–function relationship with neural networks constructed by solving a system of linear equations
title_sort	probing the structure–function relationship with neural networks constructed by solving a system of linear equations
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a33fe0176377443ea1778115dd67343f
work_keys_str_mv	AT camilojmininni probingthestructurefunctionrelationshipwithneuralnetworksconstructedbysolvingasystemoflinearequations AT bsilvanozanutto probingthestructurefunctionrelationshipwithneuralnetworksconstructedbysolvingasystemoflinearequations
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Probing the structure–function relationship with neural networks constructed by solving a system of linear equations

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