Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models.

ABSTRACT: Understanding how ensembles of neurons represent and transmit information in the patterns of their joint spiking activity is a fundamental question in computational neuroscience. At present, analyses of spiking activity from neuronal ensembles are limited because multivariate point process (MPP) models cannot represent simultaneous occurrences of spike events at an arbitrarily small time resolution. Solo recently reported a simultaneous-event multivariate point process (SEMPP) model to correct this key limitation. In this paper, we show how Solo's discrete-time formulation of the SEMPP model can be efficiently fit to ensemble neural spiking activity using a multinomial generalized linear model (mGLM). Unlike existing approximate procedures for fitting the discrete-time SEMPP model, the mGL

SUBMITTER: Ba D 

PROVIDER: S-EPMC3918645 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature