Neural coding in networks of multi-populations of neural oscillators

The paper studies the dynamical model of motor cognition of neural networks through the theory of stochastic phase resetting dynamics, presents the interaction, phase coding, and the evolution of the time-varying averaged number density in terms of populations of perceptive neurons, inter-neurons, and motor neurons subject to coupling, and probes into the dynamical reaction of neural networks under the condition of spontaneous movement and stimulation, respectively. With numerical simulations, we prove (1) Walter J. Freeman's conjecture that the response of cortex dynamics cannot code external stimulation information; (2) the possession of rhythm coding in the neural coding of serial neural networks; (3) the importance of neural inhibition in the regulation of the central nervous system.