Non-identical Neural Network Synchronization Study Based on an Adaptive Learning Rule of Synapses
YAN Chuan-Kui1,2, WANG Ru-Bin1**
1Institute for Cognitive Neurodynamics, School of Information Science and Engineering, Department of Mathematics, School of Science, East China University of Science and Technology, Shanghai 200237 2Department of Mathematics, School of Science, Hangzhou Normal University, Hangzhou 310036
Abstract：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.
. [J]. 中国物理快报, 2012, 29(9): 90501-090501.
YAN Chuan-Kui, WANG Ru-Bin. Non-identical Neural Network Synchronization Study Based on an Adaptive Learning Rule of Synapses. Chin. Phys. Lett., 2012, 29(9): 90501-090501.
2012, Vol. 29 
