Differential entropy of multivariate neural spike trains

Authors:
Nanyi Cui;Jiaying Tang;Simon R. Schultz
Affiliations:
Department of Bioengineering, Imperial College London, London, United Kingdom;Department of Bioengineering, Imperial College London, London, United Kingdom;Department of Bioengineering, Imperial College London, London, United Kingdom
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

Citing 4
Cited 0

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Abstract

Most approaches to analysing the spatiotemporal dynamics of neural populations involve binning spike trains. This is likely to underestimate the information carried by spike timing codes, in practice, if they involve high precision patterns of inter-spike intervals (ISIs). In this paper we set out to investigate the differential entropy of multivariate neural spike trains, following the work of Victor. In our framework, the unidimensional special case corresponds to estimating the differential entropy of the ISI distribution; this is generalised to multidimensional cases including patterns across successive ISIs and across cells. We investigated the differential entropy of simulated spike trains with increasing dimensionality, and applied our approach to electrophysiological data recorded from the mouse lateral geniculate nucleus.