Non-identical Neural Network Synchronization Study Based on an Adaptive Learning Rule of Synapses

An adaptive learning rule of synapses is proposed for a general asymmetric non-identical neural network. Its feasibility is proved by the Lasalle principle. Numerical simulation results show that synaptic connection weight can converge to an appropriate strength and the identical network comes to synchronization. Furthermore, by this approach of learning, a non-identical neural population can still reach synchronization. This means that the learning rule has robustness on mismatch parameters. The firing rhythm of the neural population is totally dependent on topological properties, which promotes our understanding of neuron population activities.