Name
Abstract | ||
|---|---|---|
Many neuronal systems exhibit slow random alternations and sudden switches in activity states. Models with noisy relaxation dynamics (oscillatory, excitable or bistable) account for these temporal, slow wave, patterns and the fluctuations within states. The noise-induced transitions in a relaxation dynamics are analogous to escape by a particle in a slowly changing double-well potential. In this formalism, we obtain semi-analytically the first and second order statistical properties: the distributions of the slow process at the transitions and the temporal correlations of successive switching events. We find that the temporal correlations can be used to help distinguish among biophysical mechanisms for the slow negative feedback, such as divisive or subtractive. We develop our results in the context of models for cellular pacemaker neurons; they also apply to mean-field models for spontaneously active networks with slow wave dynamics. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1186/1471-2202-9-S1-P139 | Journal of Computational Neuroscience |
Keywords | Field | DocType |
Central Pattern Generator, Silent Phase, Binocular Rivalry, Slow Manifold, Slow Wave System | Neuroscience,Computer science,Artificial intelligence | Journal |
Volume | Issue | ISSN |
9 | Suppl 1 | 1471-2202 |
Citations | PageRank | References |
6 | 0.58 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sukbin Lim | 1 | 10 | 1.07 |
| John Rinzel | 2 | 459 | 219.68 |