A Granger causality measure for point process models of ensemble neural spiking activity.

The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Grange...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sanggyun Kim, David Putrino, Soumya Ghosh, Emery N Brown
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c9fe1d0308cf4cca89993f823e953c64
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c9fe1d0308cf4cca89993f823e953c64
record_format	dspace
spelling	oai:doaj.org-article:c9fe1d0308cf4cca89993f823e953c642021-11-18T05:50:38ZA Granger causality measure for point process models of ensemble neural spiking activity.1553-734X1553-735810.1371/journal.pcbi.1001110https://doaj.org/article/c9fe1d0308cf4cca89993f823e953c642011-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21455283/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.Sanggyun KimDavid PutrinoSoumya GhoshEmery N BrownPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 3, p e1001110 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Sanggyun Kim David Putrino Soumya Ghosh Emery N Brown A Granger causality measure for point process models of ensemble neural spiking activity.
description	The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.
format	article
author	Sanggyun Kim David Putrino Soumya Ghosh Emery N Brown
author_facet	Sanggyun Kim David Putrino Soumya Ghosh Emery N Brown
author_sort	Sanggyun Kim
title	A Granger causality measure for point process models of ensemble neural spiking activity.
title_short	A Granger causality measure for point process models of ensemble neural spiking activity.
title_full	A Granger causality measure for point process models of ensemble neural spiking activity.
title_fullStr	A Granger causality measure for point process models of ensemble neural spiking activity.
title_full_unstemmed	A Granger causality measure for point process models of ensemble neural spiking activity.
title_sort	granger causality measure for point process models of ensemble neural spiking activity.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/c9fe1d0308cf4cca89993f823e953c64
work_keys_str_mv	AT sanggyunkim agrangercausalitymeasureforpointprocessmodelsofensembleneuralspikingactivity AT davidputrino agrangercausalitymeasureforpointprocessmodelsofensembleneuralspikingactivity AT soumyaghosh agrangercausalitymeasureforpointprocessmodelsofensembleneuralspikingactivity AT emerynbrown agrangercausalitymeasureforpointprocessmodelsofensembleneuralspikingactivity AT sanggyunkim grangercausalitymeasureforpointprocessmodelsofensembleneuralspikingactivity AT davidputrino grangercausalitymeasureforpointprocessmodelsofensembleneuralspikingactivity AT soumyaghosh grangercausalitymeasureforpointprocessmodelsofensembleneuralspikingactivity AT emerynbrown grangercausalitymeasureforpointprocessmodelsofensembleneuralspikingactivity
_version_	1718424815796224000

A Granger causality measure for point process models of ensemble neural spiking activity.

Ejemplares similares