UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Generating Spike Trains with Specified Correlation Coefficients

Macke, JH; Berens, P; Ecker, AS; Tolias, AS; Bethge, M; (2009) Generating Spike Trains with Specified Correlation Coefficients. NEURAL COMPUT , 21 (2) 397 - 423.

Full text not available from this repository.

Abstract

Spike trains recorded from populations of neurons can exhibit substantial pairwise correlations between neurons and rich temporal structure. Thus, for the realistic simulation and analysis of neural systems, it is essential to have efficient methods for generating artificial spike trains with specified correlation structure. Here we show how correlated binary spike trains can be simulated by means of a latent multivariate gaussian model. Sampling from the model is computationally very efficient and, in particular, feasible even for large populations of neurons. The entropy of the model is close to the theoretical maximum for a wide range of parameters. In addition, this framework naturally extends to correlations over time and offers an elegant way to model correlated neural spike counts with arbitrary marginal distributions.

Type:Article
Title:Generating Spike Trains with Specified Correlation Coefficients
Keywords:PRIMARY VISUAL-CORTEX, RETINAL GANGLION-CELLS, BINARY VARIABLES, PAIRWISE CORRELATIONS, CORTICAL-NEURONS, POPULATION CODES, MAXIMUM-ENTROPY, DISTRIBUTIONS, MICROSTIMULATION, INFORMATION
UCL classification:UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit

Archive Staff Only: edit this record