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  Generating Spike Trains with Specified Correlation Coefficients

Macke, J., Berens, P., Ecker, A., Tolias, A., & Bethge, M. (2009). Generating Spike Trains with Specified Correlation Coefficients. Neural computation, 21(2), 397-423. doi:10.1162/neco.2008.02-08-713.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C5C7-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-CA01-9
Genre: Journal Article

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 Creators:
Macke, JH1, 2, Author              
Berens, P1, 2, Author              
Ecker, AS1, 2, Author              
Tolias, AS, Author              
Bethge, M1, 2, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2009-02
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1162/neco.2008.02-08-713
BibTex Citekey: 5157
 Degree: -

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Title: Neural computation
Source Genre: Journal
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Publ. Info: Cambridge, Mass. : MIT Press
Pages: - Volume / Issue: 21 (2) Sequence Number: - Start / End Page: 397 - 423 Identifier: ISSN: 0899-7667
CoNE: https://pure.mpg.de/cone/journals/resource/954925561591