Higher-order correlations in common input shapes the output spiking activity of a neural population
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 s...
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| Formato: | Articulo |
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2017
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/160308 |
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I19-R120-10915-1603082023-11-17T20:06:46Z http://sedici.unlp.edu.ar/handle/10915/160308 Higher-order correlations in common input shapes the output spiking activity of a neural population Montangie, Lisandro Montani, Fernando Fabián 2017 2023-11-17T18:04:44Z en Física Higher-order correlations Extended Central Limit Theorem Large neural ensemble Information geometry Neuronal inputs Spiking outputs 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 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 Neuronal inputs Spiking outputs |
| spellingShingle |
Física Higher-order correlations Extended Central Limit Theorem Large neural ensemble Information geometry Neuronal inputs Spiking outputs Montangie, Lisandro Montani, Fernando Fabián Higher-order correlations in common input shapes the output spiking activity of a neural population |
| topic_facet |
Física Higher-order correlations Extended Central Limit Theorem Large neural ensemble Information geometry Neuronal inputs Spiking outputs |
| 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. |
| format |
Articulo Articulo |
| author |
Montangie, Lisandro Montani, Fernando Fabián |
| author_facet |
Montangie, Lisandro Montani, Fernando Fabián |
| author_sort |
Montangie, Lisandro |
| title |
Higher-order correlations in common input shapes the output spiking activity of a neural population |
| 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 |
| publishDate |
2017 |
| url |
http://sedici.unlp.edu.ar/handle/10915/160308 |
| work_keys_str_mv |
AT montangielisandro higherordercorrelationsincommoninputshapestheoutputspikingactivityofaneuralpopulation AT montanifernandofabian higherordercorrelationsincommoninputshapestheoutputspikingactivityofaneuralpopulation |
| _version_ |
1807221884318646272 |