Name
Title | ||
|---|---|---|
Inference of intrinsic spiking irregularity based on the Kullback-Leibler information. |
Abstract | ||
|---|---|---|
We have recently established an empirical Bayes method that extracts both the intrinsic irregularity and the time-dependent rate from a spike sequence [Koyama, S., Shinomoto, S., 2005. Empirical Bayes interpretations of random point events. J. Phys. A: Math. Gen. 38, L531–L537]. In the present paper, we examine an alternative method based on the more fundamental principle of minimizing the Kullback–Leibler information from the original distribution of spike sequences to a model distribution. Not only the empirical Bayes method but also the Kullback–Leibler information method exhibits a switch of the most plausible interpretation of the spikes between (I) being derived irregularly from a nearly constant rate, and (II) being derived rather regularly from a significantly fluctuating rate.The model distributions selected by both methods are similar for the same spike sequences derived from a given rate-fluctuating gamma process. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.biosystems.2006.05.012 | Biosystems |
Keywords | Field | DocType |
Kullback–Leibler information,Empirical Bayes method,Spiking characteristics,Firing rate estimation | Pattern recognition,Inference,Gamma process,Artificial intelligence,Empirical Bayes method,Mathematics,Machine learning,Instrumental and intrinsic value,Kullback–Leibler divergence,Bayes' theorem | Journal |
Volume | Issue | ISSN |
89 | 1 | 0303-2647 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shinsuke Koyama | 1 | 94 | 8.84 |
| Shigeru Shinomoto | 2 | 343 | 37.75 |