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dc.contributor.author
Montani, Fernando Fabián  
dc.contributor.author
Phoka, Elena  
dc.contributor.author
Portesi, Mariela Adelina  
dc.contributor.author
Schultz, Simon R.  
dc.date.available
2017-08-31T21:27:57Z  
dc.date.issued
2013-03  
dc.identifier.citation
Montani, Fernando Fabián; Phoka, Elena; Portesi, Mariela Adelina; Schultz, Simon R.; Statistical modelling of higher-order correlations in pools of neural activity; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 392; 14; 3-2013; 3066-3086  
dc.identifier.issn
0378-4371  
dc.identifier.uri
http://hdl.handle.net/11336/23406  
dc.description.abstract
Simultaneous recordings from multiple neural units allow us to investigate the activity of very large neural ensembles. To understand how large ensembles of neurons process sensory information, it is necessary to develop suitable statistical models to describe the response variability of the recorded spike trains. Using the information geometry framework, it is possible to estimate higher-order correlations by assigning one interaction parameter to each degree of correlation, leading to a (2^N-1)-dimensional model for a population with N neurons. However, this model suffers greatly from a combinatorial explosion, and the number of parameters to be estimated from the available sample size constitutes the main intractability reason of this approach. To quantify the extent of higher than pairwise spike correlations in pools of multiunit activity, we use an information-geometric approach within the framework of the extended central limit theorem considering all possible contributions from higher-order spike correlations. The identification of a deformation parameter allows us to provide a statistical characterisation of the amount of higher-order correlations in the case of a very large neural ensemble, significantly reducing the number of parameters, avoiding the sampling problem, and inferring the underlying dynamical properties of the network within pools of multiunit neural activity.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Elsevier Science  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/  
dc.subject
Neural Activity  
dc.subject
Spike Correlations  
dc.subject
High-Order Correlations  
dc.subject
Information-Geometry Approach  
dc.title
Statistical modelling of higher-order correlations in pools of neural activity  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2017-08-31T20:29:14Z  
dc.journal.volume
392  
dc.journal.number
14  
dc.journal.pagination
3066-3086  
dc.journal.pais
Países Bajos  
dc.journal.ciudad
Ámsterdam  
dc.description.fil
Fil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina  
dc.description.fil
Fil: Phoka, Elena. Imperial College London; Reino Unido  
dc.description.fil
Fil: Portesi, Mariela Adelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentina  
dc.description.fil
Fil: Schultz, Simon R.. Imperial College London; Reino Unido  
dc.journal.title
Physica A: Statistical Mechanics and its Applications  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.physa.2013.03.012  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S037843711300215X  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1211.6348