Temporal coding: metric space analysis

Metric-space analysis of spike trains: theory, algorithms, and application

Jonathan D. Victor and Keith Purpura

Network 8, 127-164 (1997)

Abstract

We present the mathematical basis of a new approach to the analysis of temporal coding. The foundation of the approach is the construction of several families of novel distances ( metrics) between neuronal impulse trains. In contrast to most previous approaches to the analysis of temporal coding, the present approach does not attempt to embed impulse trains in a vector space, and does not assume a Euclidean notion of distance. Rather, the proposed metrics formalize physiologically-based hypotheses for what aspects of the firing pattern might be stimulus-dependent, and make essential use of the point process nature of neural discharges. We show that these families of metrics endow the space of impulse trains with related but inequivalent topological structures. We show how these metrics can be used to determine whether a set of observed responses have stimulus- dependent temporal structure without a vector-space embedding. We show how multidimensional scaling can be used to assess the similarity of these metrics to Euclidean distances. For two of these families of metrics (one based on spike times and one based on spike intervals), we present highly efficient computational algorithms for calculating the distances. We illustrate these ideas by application to artificial datasets and to recordings from auditory and visual cortex.

erratum


Background on spike metrics
Fortran, matlab, and c code to calculate Dspike and Dinterval
Download the Spike Train Analysis Toolkit, a user-friendly implementation from the Laboratory of Neuroinformatics that includes algorithms for information estimation via the direct method, the binless method, and the metric space method.
Download pdf courtesy of Institute of Physics
Download from xxx.lanl.gov website
A primer on metric space analysis of spike trains
A multineuronal generalization
Publications related to temporal coding
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Erratum: The published Figure 5 ("phase discrimination") is incorrect. The information values shown in the original Figure 5 correspond to a simulation in which the phases inadvertently were set to {0, pi, 0, pi} in the four classes, i.e., the four classes contained only two distinguishable kinds of spike trains. The correct information, as calculated via the spike metrics, should be approximately twice as high for the parameters given in the text, which specified phases of {0, pi/2, pi, and 3*pi/2}. Error identified by David Goldberg, May 2004.