Efficiency characterization of a large neuronal network: A causal information approach

When inhibitory neurons constitute about 40% of neurons they could have an important antinociceptive role, as they would easily regulate the level of activity of other neurons. We consider a simple network of cortical spiking neurons with axonal conduction delays and spike timing dependent plasticit...

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Autores principales: Montani, Fernando Fabián, Deleglise, Emilia, Rosso, Osvaldo A.
Formato: Articulo
Lenguaje:Inglés
Publicado: 2014
Materias:
Ciencias Exactas
Física
Neural dynamics
Permutation entropy
Complexity
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/131426
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-131426
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Exactas
Física
Neural dynamics
Permutation entropy
Complexity
spellingShingle Ciencias Exactas
Física
Neural dynamics
Permutation entropy
Complexity
Montani, Fernando Fabián
Deleglise, Emilia
Rosso, Osvaldo A.
Efficiency characterization of a large neuronal network: A causal information approach
topic_facet Ciencias Exactas
Física
Neural dynamics
Permutation entropy
Complexity
description When inhibitory neurons constitute about 40% of neurons they could have an important antinociceptive role, as they would easily regulate the level of activity of other neurons. We consider a simple network of cortical spiking neurons with axonal conduction delays and spike timing dependent plasticity, representative of a cortical column or hypercolumn with a large proportion of inhibitory neurons. Each neuron fires following a Hodgkin–Huxley like dynamics and it is interconnected randomly to other neurons. The network dynamics is investigated estimating Bandt and Pompe probability distribution function associated to the interspike intervals and taking different degrees of interconnectivity across neurons. More specifically we take into account the fine temporal “structures” of the complex neuronal signals not just by using the probability distributions associated to the interspike intervals, but instead considering much more subtle measures accounting for their causal information: the Shannon permutation entropy, Fisher permutation information and permutation statistical complexity. This allows us to investigate how the information of the system might saturate to a finite value as the degree of interconnectivity across neurons grows, inferring the emergent dynamical properties of the system.
format Articulo
Articulo
author Montani, Fernando Fabián
Deleglise, Emilia
Rosso, Osvaldo A.
author_facet Montani, Fernando Fabián
Deleglise, Emilia
Rosso, Osvaldo A.
author_sort Montani, Fernando Fabián
title Efficiency characterization of a large neuronal network: A causal information approach
title_short Efficiency characterization of a large neuronal network: A causal information approach
title_full Efficiency characterization of a large neuronal network: A causal information approach
title_fullStr Efficiency characterization of a large neuronal network: A causal information approach
title_full_unstemmed Efficiency characterization of a large neuronal network: A causal information approach
title_sort efficiency characterization of a large neuronal network: a causal information approach
publishDate 2014
url http://sedici.unlp.edu.ar/handle/10915/131426
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AT rossoosvaldoa efficiencycharacterizationofalargeneuronalnetworkacausalinformationapproach
bdutipo_str Repositorios
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