Generating spike trains with specified correlation coefficients

Macke, J. H.; Berens, P.; Ecker, A. S.; Tolias, A. S.; Bethge, M.

doi:10.1162/neco.2008.02-08-713

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Generating spike trains with specified correlation coefficients

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

http://www.ncbi.nlm.nih.gov/pubmed/19196233
(Any fulltext)

http://www.mitpressjournals.org/doi/abs/10.1162/neco.2008.02-08-713?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dpubmed&
(Any fulltext)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Macke, J. H., Berens, P., Ecker, A. S., Tolias, A. S., & Bethge, M. (2009). Generating spike trains with specified correlation coefficients. Neural Computation, 21(2), 397-423. doi:10.1162/neco.2008.02-08-713.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0028-64EF-9

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.