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Phys Rev E Stat Nonlin Soft Matter Phys. 2002 Nov;66(5 Pt 1):051903. Epub 2002 Nov 11.

Binless strategies for estimation of information from neural data.

Author information

1
Department of Neurology and Neuroscience, Weill Medical College of Cornell University, 1300 York Avenue, New York, New York 10021, USA. jdvicto@med.cornell.edu

Abstract

We present an approach to estimate information carried by experimentally observed neural spike trains elicited by known stimuli. This approach makes use of an embedding of the observed spike trains into a set of vector spaces, and entropy estimates based on the nearest-neighbor Euclidean distances within these vector spaces [L. F. Kozachenko and N. N. Leonenko, Probl. Peredachi Inf. 23, 9 (1987)]. Using numerical examples, we show that this approach can be dramatically more efficient than standard bin-based approaches such as the "direct" method [S. P. Strong, R. Koberle, R. R. de Ruyter van Steveninck, and W. Bialek, Phys. Rev. Lett. 80, 197 (1998)] for amounts of data typically available from laboratory experiments.

PMID:
12513519
DOI:
10.1103/PhysRevE.66.051903
[Indexed for MEDLINE]

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