Important questions in neuroscience, such as how neural activity represents the sensory world, can be framed in terms of the extent to which spike trains differ from one another. Since spike trains can be considered to be sequences of stereotyped events, it is natural to focus on ways to quantify differences between event sequences, known as spike-train metrics. We begin by defining several families of these metrics, including metrics based on spike times, on interspike intervals, and on vector-space embedding. We show how these metrics can be applied to single-neuron and multineuronal data and then describe algorithms that calculate these metrics efficiently. Finally, we discuss analytical procedures based on these metrics, including methods for quantifying variability among spike trains, for constructing perceptual spaces, for calculating information-theoretic quantities, and for identifying candidate features of neural codes.
KeywordsMutual Information Spike Train Dynamic Programming Algorithm Elementary Step Interspike Interval
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- Dubbs AJ, Seiler BA, Magnasco MO (2009) A fast Lp spike alignment metric. arXiv:0907.3137v2
- Gaal SA (1964) Point set topology. Academic Press, New York Google Scholar
- Kruskal JB, Wish M (1978) Multidimensional scaling. Sage, Beverly Hills Google Scholar
- Miller GA (1955) Note on the bias on information estimates. Information Theory in Psychology: Problems and Methods II-B:95–100 Google Scholar
- Nelken I (2009) Personal communication Google Scholar
- Reich D, Mechler F, Victor J (2000) Temporal coding of contrast in primary visual cortex: when, what, and why?. J Neurophysiol 85:1039–1050 Google Scholar
- Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W (1997) Spikes: exploring the neural code. MIT Press, Cambridge Google Scholar
- Segundo JP, Perkel DH (1969) The nerve cell as an analyzer of spike trains. In: Brazier MAB (ed) The interneuron. University of California Press, Berkeley, pp 349–390 Google Scholar
- Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, Urbana Google Scholar