Higher-order correlations in common input shapes the output spiking activity of a neural population
- Autores
- Montangie, Lisandro; Montani, Fernando Fabián
- Año de publicación
- 2017
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from -Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter . We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.
Instituto de Física de Líquidos y Sistemas Biológicos - Materia
-
Física
Higher-order correlations
Extended Central Limit Theorem
Large neural ensemble
Information geometry
Neuronal inputs
Spiking outputs - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/160308
Ver los metadatos del registro completo
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Higher-order correlations in common input shapes the output spiking activity of a neural populationMontangie, LisandroMontani, Fernando FabiánFísicaHigher-order correlationsExtended Central Limit TheoremLarge neural ensembleInformation geometryNeuronal inputsSpiking outputsRecent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from -Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter . We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.Instituto de Física de Líquidos y Sistemas Biológicos2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/160308enginfo:eu-repo/semantics/altIdentifier/issn/0378-4371info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2016.12.002info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:22:53Zoai:sedici.unlp.edu.ar:10915/160308Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:22:54.112SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
Higher-order correlations in common input shapes the output spiking activity of a neural population |
| title |
Higher-order correlations in common input shapes the output spiking activity of a neural population |
| spellingShingle |
Higher-order correlations in common input shapes the output spiking activity of a neural population Montangie, Lisandro Física Higher-order correlations Extended Central Limit Theorem Large neural ensemble Information geometry Neuronal inputs Spiking outputs |
| title_short |
Higher-order correlations in common input shapes the output spiking activity of a neural population |
| title_full |
Higher-order correlations in common input shapes the output spiking activity of a neural population |
| title_fullStr |
Higher-order correlations in common input shapes the output spiking activity of a neural population |
| title_full_unstemmed |
Higher-order correlations in common input shapes the output spiking activity of a neural population |
| title_sort |
Higher-order correlations in common input shapes the output spiking activity of a neural population |
| dc.creator.none.fl_str_mv |
Montangie, Lisandro Montani, Fernando Fabián |
| author |
Montangie, Lisandro |
| author_facet |
Montangie, Lisandro Montani, Fernando Fabián |
| author_role |
author |
| author2 |
Montani, Fernando Fabián |
| author2_role |
author |
| dc.subject.none.fl_str_mv |
Física Higher-order correlations Extended Central Limit Theorem Large neural ensemble Information geometry Neuronal inputs Spiking outputs |
| topic |
Física Higher-order correlations Extended Central Limit Theorem Large neural ensemble Information geometry Neuronal inputs Spiking outputs |
| dc.description.none.fl_txt_mv |
Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from -Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter . We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs. Instituto de Física de Líquidos y Sistemas Biológicos |
| description |
Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from -Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter . We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs. |
| publishDate |
2017 |
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2017 |
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eng |
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eng |
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info:eu-repo/semantics/altIdentifier/issn/0378-4371 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2016.12.002 |
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