dorsal/arxiv
View SchemaMetric-space analysis of spike trains: theory, algorithms, and application
| Authors | Jonathan D. Victor, Keith P. Purpura |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0309031 |
| URL | https://arxiv.org/abs/q-bio/0309031 |
| Journal | 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.
{
"annotation_id": "a4226e3b-18be-41cc-9c92-f151da0a7276",
"date_created": "2026-03-02T18:01:28.424000Z",
"date_modified": "2026-03-02T18:01:28.424000Z",
"file_hash": "5a77d9f705dfe15a573cf7f3a3cc443659416722bcaa219844b3b0c5b814c067",
"private": false,
"record": {
"abstract": "We present the mathematical basis of a new approach to the analysis of\ntemporal coding. The foundation of the approach is the construction of several\nfamilies of novel distances (metrics) between neuronal impulse trains. In\ncontrast to most previous approaches to the analysis of temporal coding, the\npresent approach does not attempt to embed impulse trains in a vector space,\nand does not assume a Euclidean notion of distance. Rather, the proposed\nmetrics formalize physiologically-based hypotheses for what aspects of the\nfiring pattern might be stimulus-dependent, and make essential use of the point\nprocess nature of neural discharges. We show that these families of metrics\nendow the space of impulse trains with related but inequivalent topological\nstructures. We show how these metrics can be used to determine whether a set of\nobserved responses have stimulus-dependent temporal structure without a\nvector-space embedding. We show how multidimensional scaling can be used to\nassess the similarity of these metrics to Euclidean distances. For two of these\nfamilies of metrics (one based on spike times and one based on spike\nintervals), we present highly efficient computational algorithms for\ncalculating the distances. We illustrate these ideas by application to\nartificial datasets and to recordings from auditory and visual cortex.",
"arxiv_id": "q-bio/0309031",
"authors": [
"Jonathan D. Victor",
"Keith P. Purpura"
],
"categories": [
"q-bio.NC",
"q-bio.QM"
],
"journal_ref": "Network 8, 127-164 (1997)",
"title": "Metric-space analysis of spike trains: theory, algorithms, and application",
"url": "https://arxiv.org/abs/q-bio/0309031"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "4c2e9cfe-571a-4b44-a9c8-093992f2f9c5",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}