Quantifying higher-order correlations in a neuronal pool

Recent experiments involving a relatively large population of neurons have shown a very significant amount of higher-order correlations. However, little is known of how these affect the integration and firing behavior of a population of neurons beyond the second order statistics. To investigate how...

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Detalles Bibliográficos
Autores principales: Montangie, Lisandro, Montani, Fernando Fabián
Formato: Articulo
Lenguaje:Inglés
Publicado: 2015
Materias:
Física
Higher order correlations
Extended central limit theorem
Large neural ensemble
Information geometry
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/160303
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-160303
record_format dspace
spelling I19-R120-10915-1603032023-11-17T20:06:49Z http://sedici.unlp.edu.ar/handle/10915/160303 Quantifying higher-order correlations in a neuronal pool Montangie, Lisandro Montani, Fernando Fabián 2015 2023-11-17T17:33:18Z en Física Higher order correlations Extended central limit theorem Large neural ensemble Information geometry Recent experiments involving a relatively large population of neurons have shown a very significant amount of higher-order correlations. However, little is known of how these affect the integration and firing behavior of a population of neurons beyond the second order statistics. To investigate how higher-order inputs statistics can shape beyond pairwise spike correlations and affect information coding in the brain, we consider a neuronal pool where each neuron fires stochastically. We develop a simple mathematically tractable model that makes it feasible to account for higher-order spike correlations in a neuronal pool with highly interconnected common inputs beyond second order statistics. In our model, correlations between neurons appear from q-Gaussian inputs into threshold neurons. The approach constitutes the natural extension of the Dichotomized Gaussian model, where the inputs to the model are just Gaussian distributed and therefore have no input interactions beyond second order. We obtain an exact analytical expression for the joint distribution of firing, quantifying the degree of higher-order spike correlations, truly emphasizing the functional aspects of higher-order statistics, as we account for beyond second order inputs correlations seen by each neuron within the pool. We determine how higherorder correlations depend on the interaction structure of the input, showing that the joint distribution of firing is skewed as the parameter q increases inducing larger excursions of synchronized spikes. We show how input nonlinearities can shape higher-order correlations and enhance coding performance by neural populations. Instituto de Física de Líquidos y Sistemas Biológicos 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
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Física
Higher order correlations
Extended central limit theorem
Large neural ensemble
Information geometry
spellingShingle Física
Higher order correlations
Extended central limit theorem
Large neural ensemble
Information geometry
Montangie, Lisandro
Montani, Fernando Fabián
Quantifying higher-order correlations in a neuronal pool
topic_facet Física
Higher order correlations
Extended central limit theorem
Large neural ensemble
Information geometry
description Recent experiments involving a relatively large population of neurons have shown a very significant amount of higher-order correlations. However, little is known of how these affect the integration and firing behavior of a population of neurons beyond the second order statistics. To investigate how higher-order inputs statistics can shape beyond pairwise spike correlations and affect information coding in the brain, we consider a neuronal pool where each neuron fires stochastically. We develop a simple mathematically tractable model that makes it feasible to account for higher-order spike correlations in a neuronal pool with highly interconnected common inputs beyond second order statistics. In our model, correlations between neurons appear from q-Gaussian inputs into threshold neurons. The approach constitutes the natural extension of the Dichotomized Gaussian model, where the inputs to the model are just Gaussian distributed and therefore have no input interactions beyond second order. We obtain an exact analytical expression for the joint distribution of firing, quantifying the degree of higher-order spike correlations, truly emphasizing the functional aspects of higher-order statistics, as we account for beyond second order inputs correlations seen by each neuron within the pool. We determine how higherorder correlations depend on the interaction structure of the input, showing that the joint distribution of firing is skewed as the parameter q increases inducing larger excursions of synchronized spikes. We show how input nonlinearities can shape higher-order correlations and enhance coding performance by neural populations.
format Articulo
Articulo
author Montangie, Lisandro
Montani, Fernando Fabián
author_facet Montangie, Lisandro
Montani, Fernando Fabián
author_sort Montangie, Lisandro
title Quantifying higher-order correlations in a neuronal pool
title_short Quantifying higher-order correlations in a neuronal pool
title_full Quantifying higher-order correlations in a neuronal pool
title_fullStr Quantifying higher-order correlations in a neuronal pool
title_full_unstemmed Quantifying higher-order correlations in a neuronal pool
title_sort quantifying higher-order correlations in a neuronal pool
publishDate 2015
url http://sedici.unlp.edu.ar/handle/10915/160303
work_keys_str_mv AT montangielisandro quantifyinghigherordercorrelationsinaneuronalpool
AT montanifernandofabian quantifyinghigherordercorrelationsinaneuronalpool
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