Name
Abstract | ||
|---|---|---|
We focus in this paper on the methodology of building statistically valid population code read-out models for spike train data. A new method is explored, which uses Bayesian networks to formalize the read-out model, Monte Carlo validation to check the statistical validity of the model and scrambled quasi-random vectors to speed up the validation process. This procedure avoids imposing usual additional constraints on the data. We present the method through an application in the context of non-metric categorical vision-related data. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1016/S0925-2312(02)00349-1 | Neurocomputing |
Keywords | Field | DocType |
Bayesian networks,Category decoding,Information,Population code | Population,Monte Carlo method,Validity,Spike train,Categorical variable,Computer science,Bayesian network,Artificial intelligence,Decoding methods,Machine learning,Speedup | Journal |
Volume | ISSN | Citations |
44 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Andras | 1 | 19 | 5.11 |
| Stefano Panzeri | 2 | 404 | 62.09 |
| malcolm p young | 3 | 23 | 5.19 |