Name
Abstract | ||
|---|---|---|
In many cortical areas, neural spike trains do not follow a Poisson process. In this study, we investigate a possible benefit of non-Poisson spiking for information transmission by studying the minimal rate fluctuation that can be detected by a Bayesian estimator. The idea is that an inhomogeneous Poisson process may make it difficult for downstream decoders to resolve subtle changes in rate fluctuation, but by using a more regular non-Poisson process, the nervous system can make rate fluctuations easier to detect. We evaluate the degree to which regular firing reduces the rate fluctuation detection threshold. We find that the threshold for detection is reduced in proportion to the coefficient of variation of interspike intervals. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1162/NECO_a_00420 | Neural Computation |
Keywords | Field | DocType |
rate fluctuation,minimal rate fluctuation,rate fluctuation detection threshold,Poisson process,inhomogeneous Poisson process,regular non-Poisson process,non-Poisson spiking,regular firing,Bayesian estimator,cortical area,information transmission,non-poisson regular firing | Inhomogeneous Poisson process,Information transmission,Bayesian estimator,Artificial intelligence,Poisson distribution,Poisson process,Machine learning,Mathematics,Bayes' theorem | Journal |
Volume | Issue | ISSN |
25 | 4 | 1530-888X |
Citations | PageRank | References |
2 | 0.42 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shinsuke Koyama | 1 | 94 | 8.84 |
| Takahiro Omi | 2 | 14 | 1.38 |
| Robert E. Kass | 3 | 328 | 43.43 |
| Shigeru Shinomoto | 4 | 343 | 37.75 |