Some theoretical results on neural spike train probability models
| dc.creator | Chan, Hock Peng | |
| dc.creator | Loh, Wei-Liem | |
| dc.date | 2007-03-28 | |
| dc.date.accessioned | 2026-07-07T08:08:52Z | |
| dc.date.available | 2026-07-07T08:08:52Z | |
| dc.description | This article contains two main theoretical results on neural spike train models. The first assumes that the spike train is modeled as a counting or point process on the real line where the conditional intensity function is a product of a free firing rate function s, which depends only on the stimulus, and a recovery function r, which depends only on the time since the last spike. If s and r belong to a q-smooth class of functions, it is proved that sieve maximum likelihood estimators for s and r achieve essentially the optimal convergence rate (except for a logarithmic factor) under L_1 loss. The second part of this article considers template matching of multiple spike trains. P-values for the occurrences of a given template or pattern in a set of spike trains are computed using a general scoring system. By identifying the pattern with an experimental stimulus, multiple spike trains can be deciphered to provide useful information. | |
| dc.description | 55 pages | |
| dc.identifier | https://arxiv.org/abs/math/0703829 | |
| dc.identifier | http://arxiv.org/abs/math/0703829 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/131409 | |
| dc.subject | Statistics Theory | |
| dc.subject | Primary 62E20; secondary 62G20, 62M20 | |
| dc.title | Some theoretical results on neural spike train probability models | |
| dc.type | text |