STOCHASTICAL ASPECTS OF NEURONAL DYNAMICS - FOKKER-PLANCK APPROACH

The stochastical aspects of noise-perturbed neuronal dynamics are stud ied via the Fokker-Planck equation by considering the Langevin-type re laxational, nonlinear process associated with neuronal states. On the basis of a canonical, stochastically driven, dichotomous state modelin g, the equilibrium conditions in the neuronal assembly are analyzed. T he markovian structure of the random occurrence of action potentials d ue to the disturbances (noise) in the neuronal state is considered, an d the corresponding solutions relevant to the colored noise spectrum o f the disturbance effects are addressed. Stochastical instability (Lya punov) considerations in solving discrete optimization problems via ne ural networks are discussed. The bounded estimate(s) of the stochastic al variates involved are presented, and the noise-induced perturbation s on the saturated-state neuronal population are elucidated.