Extended method of moments for deterministic analysis of stochastic multistable neurodynamical systems
- Computational Neuroscience Group, Universitat Pompeu Fabra, Passeig de Circumvallacio, 8, 08003 Barcelona (Spain)
The analysis of transitions in stochastic neurodynamical systems is essential to understand the computational principles that underlie those perceptual and cognitive processes involving multistable phenomena, like decision making and bistable perception. To investigate the role of noise in a multistable neurodynamical system described by coupled differential equations, one usually considers numerical simulations, which are time consuming because of the need for sufficiently many trials to capture the statistics of the influence of the fluctuations on that system. An alternative analytical approach involves the derivation of deterministic differential equations for the moments of the distribution of the activity of the neuronal populations. However, the application of the method of moments is restricted by the assumption that the distribution of the state variables of the system takes on a unimodal Gaussian shape. We extend in this paper the classical moments method to the case of bimodal distribution of the state variables, such that a reduced system of deterministic coupled differential equations can be derived for the desired regime of multistability.
- OSTI ID:
- 21072399
- Journal Information:
- Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, Vol. 75, Issue 3; Other Information: DOI: 10.1103/PhysRevE.75.031913; (c) 2007 The American Physical Society; Country of input: International Atomic Energy Agency (IAEA); ISSN 1063-651X
- Country of Publication:
- United States
- Language:
- English
Similar Records
Validity conditions for moment closure approximations in stochastic chemical kinetics
Comparison of different moment-closure approximations for stochastic chemical kinetics