title: Macroscopic dynamics in separable neural networks.
abstract: Parallel dynamics of neural networks with separable coupling is given starting from Coolen-Sherrington theory. Away from saturation, it is shown that this parallel retrieve dynamics is equivalent to the sequential dynamics for finite temperature. But the finite-size effects were found to be governed by a homogeneous Markov process, not by a time-dependent Ornstein-Uhlenbeck process in sequential dynamics.
authors: Department of Physics, Lanzhou University, Lanzhou Gansu 730000, China.
authors: C
authors: C
authors: Yong
authors: W
authors: W
authors: Yinghai
authors: Y
authors: Y
authors: Kongqing
mesh_terms: D001703:Biophysics / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D009415:Nerve Net; D013269:Stochastic Processes; D013696:Temperature; D013997:Time Factors