- Poster Presentation
- Open Access
A new spike train metric
© Rusu and Florian; licensee BioMed Central Ltd. 2010
- Published: 20 July 2010
- Topological Property
- Spike Train
- Actual Position
- Hausdorff Distance
- Mathematical Point
The theoretical and practical importance of quantifying the degree of similarity between pairs of spike trains has resulted in a plethora of spike train metrics. Some are based on cost functions [1, 2] while others use smoothing kernels  or binning techniques  and then rely on spike intervals or precise spike timings to compute the distance. Spike metrics are especially important as they enable the analysis of the neural code, a fundamental and heavily debated issue in neuroscience. Here, we introduce a new class of spike train metrics dependent on smooth kernels. They compute the distance between pairs of spike trains and yield a result that is non-linearly dependent on the precise timing of the differences across the two spike trains. In this situation, the actual position of a spike outweighs the importance of the inter-spike interval. In the exchange of information between two neurons each spike may be as important as the spike train itself  and therefore, metrics based on the specific timing of differences are desirable.
The introduced spike train metrics, which will be referred to as max-metrics, are similar to the Hausdorff distance between two non-empty compact sets. They are given in two distinct forms: one that uses a convolution kernel to filter each spike train and, the other that uses the raw spike times. Because the latter does not rely on a smoothing kernel and uses the spike train directly, it does not introduce additional time constants and therefore has the advantage that it is more general. From a mathematical point of view the kernels can be just about any function because the generated metrics are commensurable. Some, however, will have a lesser physiological interpretation than others. The space of spike trains endowed with either form of the max-metric is compact. The implication for learning is that any learning rule based on the metric will eventually converge to a point in the spike train space. Because the max-metric generates the same topology regardless of the choice of kernels, topological properties such as compactness are common to all spike train spaces. The metrics are benchmarked against a simple spike count distance and against the original and a modified version of the van Rossum metric .
Supported by The Sectorial Operational Programme Human Resources Development (Contract POSDRU 6/1.5/S/3) and a grant of the Romanian National Authority for Scientific Research (PNCDI II, Parteneriate, contract no. 11-039/2007).
- Victor JD, Purpura K: Nature and precision of temporal coding in visual cortex: A metric-space analysis. Journal of Neurophysiology. 1996, 76: 1310-1326.PubMedGoogle Scholar
- Victor JD, Purpura K: Metric-space analysis of spike trains: Theory, algorithms and application. Network: Computation in Neural Systems. 1997, 8: 127-164. 10.1088/0954-898X/8/2/003.View ArticleGoogle Scholar
- van Rossum MCW: A novel spike train distance. Neural Computation. 2001, 13: 751-763. 10.1162/089976601300014321.View ArticlePubMedGoogle Scholar
- Geisler WS, Albrecht DG, Salvi RJ, Saunders SS: Discrimination performance of single neurons: Rate and temporal information. Journal of Neurophysiology. 1991, 66: 334-362.PubMedGoogle Scholar
- Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W: Spikes: Exploring the neural code. 1997, Cambridge: MIT PressGoogle Scholar
This article is published under license to BioMed Central Ltd.