The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex

Understanding the operations of neural networks in the brain requires an understanding of whether interactions among neurons can be described by a pairwise interaction model, or whether a higher order interaction model is needed. In this article we consider the rate of synchronous discharge of a loc...

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Autores principales: Montani, Fernando Fabián, Ince, Rob A. A., Senatore, Riccardo, Arabzadeh, Ehsan, Diamond, Mathew E., Panzeri, Stefano
Formato: Articulo
Lenguaje:Inglés
Publicado: 2009
Materias:
Física
Ciencias Médicas
spike synchronization
cortex
information geometry
maximum entropy
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/160254
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-160254
record_format dspace
spelling I19-R120-10915-1602542023-11-16T20:06:53Z http://sedici.unlp.edu.ar/handle/10915/160254 The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex Montani, Fernando Fabián Ince, Rob A. A. Senatore, Riccardo Arabzadeh, Ehsan Diamond, Mathew E. Panzeri, Stefano 2009 2023-11-16T16:28:04Z en Física Ciencias Médicas spike synchronization cortex information geometry maximum entropy Understanding the operations of neural networks in the brain requires an understanding of whether interactions among neurons can be described by a pairwise interaction model, or whether a higher order interaction model is needed. In this article we consider the rate of synchronous discharge of a local population of neurons, a macroscopic index of the activation of the neural network that can be measured experimentally. We analyse a model based on physics’ maximum entropy principle that evaluates whether the probability of synchronous discharge can be described by interactions up to any given order. When compared with real neural population activity obtained from the rat somatosensory cortex, the model shows that interactions of at least order three or four are necessary to explain the data. We use Shannon information to compute the impact of high-order correlations on the amount of somatosensory information transmitted by the rate of synchronous discharge, and we find that correlations of higher order progressively decrease the information available through the neural population. These results are compatible with the hypothesis that high-order interactions play a role in shaping the dynamics of neural networks, and that they should be taken into account when computing the representational capacity of neural populations. Facultad de Ciencias Exactas Articulo Articulo http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 3297–3310
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Física
Ciencias Médicas
spike synchronization
cortex
information geometry
maximum entropy
spellingShingle Física
Ciencias Médicas
spike synchronization
cortex
information geometry
maximum entropy
Montani, Fernando Fabián
Ince, Rob A. A.
Senatore, Riccardo
Arabzadeh, Ehsan
Diamond, Mathew E.
Panzeri, Stefano
The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex
topic_facet Física
Ciencias Médicas
spike synchronization
cortex
information geometry
maximum entropy
description Understanding the operations of neural networks in the brain requires an understanding of whether interactions among neurons can be described by a pairwise interaction model, or whether a higher order interaction model is needed. In this article we consider the rate of synchronous discharge of a local population of neurons, a macroscopic index of the activation of the neural network that can be measured experimentally. We analyse a model based on physics’ maximum entropy principle that evaluates whether the probability of synchronous discharge can be described by interactions up to any given order. When compared with real neural population activity obtained from the rat somatosensory cortex, the model shows that interactions of at least order three or four are necessary to explain the data. We use Shannon information to compute the impact of high-order correlations on the amount of somatosensory information transmitted by the rate of synchronous discharge, and we find that correlations of higher order progressively decrease the information available through the neural population. These results are compatible with the hypothesis that high-order interactions play a role in shaping the dynamics of neural networks, and that they should be taken into account when computing the representational capacity of neural populations.
format Articulo
Articulo
author Montani, Fernando Fabián
Ince, Rob A. A.
Senatore, Riccardo
Arabzadeh, Ehsan
Diamond, Mathew E.
Panzeri, Stefano
author_facet Montani, Fernando Fabián
Ince, Rob A. A.
Senatore, Riccardo
Arabzadeh, Ehsan
Diamond, Mathew E.
Panzeri, Stefano
author_sort Montani, Fernando Fabián
title The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex
title_short The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex
title_full The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex
title_fullStr The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex
title_full_unstemmed The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex
title_sort impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex
publishDate 2009
url http://sedici.unlp.edu.ar/handle/10915/160254
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