Binless Estimation of Information in Spike Trains

Binless strategies for estimation of information from neural data

Jonathan D. Victor

Phys. Rev. E 66, 51903 (2002)


We present a novel 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 (Kozachenko, L.F., and Leonenko, N.N. (1987) Sample estimate of the entropy of a random vector. Problemy Peredachi Informatsii, vol. 23, no. 2, pp. 9-16). Via numerical examples, we show that this approach can be dramatically more efficient than standard bin-based approaches such as the direct method (Strong, S.P., Koberle, R., Ruyter van Steveninck, R.R. de, and Bialek, W. (1998) Entropy and information in neural spike trains. Phys. Rev. Lett. 80, 197-200) for amounts of data typically available from laboratory experiments.

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Erratum: The signatures on the first 3 terms in eqs. 15 and 22 are inverted. Thanks to David Goldberg.
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Download the Spike Train Analysis Toolkit, a user-friendly implementation from the Laboratory of Neuroinformatics that includes algorithms for information estimation via the direct method, the binless method, and the metric space method.
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