Asymmetric neural network synchronization and dynamics based on an adaptive learning rule of synapses

An adaptive learning rule of synapses was proposed for a general asymmetric neural network. Its feasibility was proved by the Lasalle principle. Numerical simulation results show that synaptic connection weight can converge to an appropriate strength and the network comes to synchronization. Furthermore, ISI (inter-spike interval) of synchronization orbit in neural network has a typical period doubling bifurcation. It is a further improvement compared with bifurcation of the traditional single neuron model, which promotes our understanding of neuron population activities.