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.
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Cui, N., Tang, J., Schultz, S.R. (2012). Differential Entropy of Multivariate Neural Spike Trains. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_36
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DOI: https://doi.org/10.1007/978-3-642-33269-2_36
Publisher Name: Springer, Berlin, Heidelberg
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