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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 461684.0, MPI für biologische Kybernetik / Biologische Kybernetik
Generating Spike Trains with Specified Correlation Coefficients
Authors:Macke, J.H.; Berens, P.; Ecker, A.S.; Tolias, A.S.; Bethge, M.
Date of Publication (YYYY-MM-DD):2009-02
Title of Journal:Neural Computation
Volume:21
Issue / Number:2
Start Page:397
End Page:423
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Article
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
MPI für biologische Kybernetik/NWG Bethge
Identifiers:LOCALID:5157
URL:http://www.mitpressjournals.org/doi/pdf/10.1162/ne...
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