a granger causality measure for point process models of ensemble neural spiking activity点过程模型的格兰杰因果测量合奏神经活动激增.pdfVIP

A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity 1 2 3 1,2,4 Sanggyun Kim *, David Putrino , Soumya Ghosh , Emery N. Brown 1 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America, 2 Department of Anesthesia and Critical Care, Massachusetts General Hospital, Boston, Massachusetts, United States of America, 3 Centre for Neuromuscular and Neurological Disorders, University of Western Australia, QEII Medical Centre, Nedlands, Australia, 4 Division of Health Sciences and Technology, Harvard Medical School/Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America Abstract 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 comp