Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Spike Train Distance

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_409-1

Synonyms

Definition

A “spike train distance” (or, equivalently, a “spike metric”) is a means for comparing two samples of stereotyped event sequences. While spike train distances can be applied to any kind of stereotyped event sequence, we focus here on their application to neuroscience, in which the event sequences represent the sequence of action potentials emitted by a neuron or a set of neurons.

Detailed Description

Overview

Spike train distances are “metrics,” namely, rules for assigning a notion of distance, or dissimilarity, to elements in a topological space. Two considerations give this general framework a special flavor when applied to neural data. The first consideration is mathematical: the topology of event sequences combines a discrete component with a continuous component. The discrete component is that the number of events in a spike train must be an integer; the continuous component is that each of these events can occur across a continuum of times. The...

Keywords

Depression Convolution Haas Purpura Milton 
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Notes

Acknowledgments

The author thanks Conor Houghton and Thomas Kreuz for their very helpful comments on this entry. This work was supported by NIH NEI grant EY09314.

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Further Reading

  1. Dubbs AJ, Seiler BA, Magnasco MO (2010) A fast L(p) spike alignment metric. Neural Comput 22(11):2785–2808CrossRefPubMedGoogle Scholar
  2. Houghton C, Victor JD (2011) Measuring representational distances – the spike train metrics approach. In: Kriegeskorte N, Kreiman G (eds) Understanding visual population coes – towards a common multivariate framework for cell recording and functional imaging. MIT Press, Cambridge, MAGoogle Scholar
  3. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48(3):443–453CrossRefPubMedGoogle Scholar
  4. Van Rossum MC (2001) A novel spike distance. Neural Comput 13(4):751–763CrossRefPubMedGoogle Scholar
  5. Victor JD, Purpura KP (1997) Metric-space analysis of spike trains: theory, algorithms and application. Network 8:127–164CrossRefGoogle Scholar
  6. Victor JD, Purpura KP (2010) Spike Metrics. In: Rotter S, Gruen S (eds) Analysis of parallel spike trains. Springer, New York/HeidelbergGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Brain and Mind Research Institute and Department of NeurologyWeill Cornell Medical College of Cornell UniversityNew YorkUSA