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Temporal correlations and neural spike train entropy

Lookup NU author(s): Dr Stefano Panzeri

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Abstract

Sampling considerations limit the experimental conditions under which information theoretic analyses of neurophysiological data yield reliable results. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for limited samples of data. This approach also yields insight to the role of correlations between spikes in temporal coding mechanisms. The method, when applied to recordings from complex cells of the monkey primary visual cortex, results in lower rms error information estimates in comparison to a "brute force" approach.

Publication metadata

Author(s): Schultz SR, Panzeri S

Publication type: Article

Publication status: Published

Journal: Physical Review Letters

Year: 2001

Volume: 86

Issue: 25

Pages: 5823-5826

ISSN (print): 0031-9007

ISSN (electronic): 1079-7114

Publisher: American Physical Society

URL: http://dx.doi.org/10.1103/PhysRevLett.86.5823

DOI: 10.1103/PhysRevLett.86.5823

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