Name
Title | ||
|---|---|---|
A Bayesian decision approach to evaluate local and contextual information in spike trains |
Abstract | ||
|---|---|---|
In this study, we applied Bayesian decision theory to evaluate the information contained in neural spike trains. We used the spike statistics from 90% of the labelled trials to classify each of the remaining unlabelled trials. Classification rate were computed at different post-stimulus time within time windows of different durations. This allowed us to visualize and evaluate the information content of the spike trains in a scale-space representation. We found that discrimination of patterns within the receptive fields of the neurons can be accomplished at an early stage of the response within a relatively small time window (5-30 ms), while the discrimination of global contextual information can be accomplished at a later time. (C) 2000 Elsevier Science B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1016/S0925-2312(00)00273-3 | NEUROCOMPUTING |
Keywords | Field | DocType |
scale-space,information,neural data analysis,Bayes decision | Receptive field,Contextual information,Pattern recognition,Computer science,Scale space,Artificial intelligence,Train,Bayes estimator,Classification rate,Machine learning,Bayesian probability | Journal |
Volume | Issue | ISSN |
32 | null | 0925-2312 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| elise cassidente | 1 | 0 | 0.34 |
| xiaogang yan | 2 | 0 | 0.34 |
| Tai Sing Lee | 3 | 794 | 88.73 |