Computers & Mathematics with Applications
Multistability in networks with self-excitation and high-order synaptic connectivity
IEEE Transactions on Circuits and Systems Part I: Regular Papers
IEEE Transactions on Neural Networks
Dynamics of competitive neural networks with inverse lipschitz neuron activations
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
| Hi-index | 0.00 |
In this paper, we study the global exponential stability of a multitime scale competitive neural network model with nonsmooth functions, which models a literally inhibited neural network with unsupervised Hebbian learning. The network has two types of state variables, one corresponds to the fast neural activity and another to the slow unsupervised modification of connection weights. Based on the nonsmooth analysis techniques, we prove the existence and uniqueness of equilibrium for the system and establish some new theoretical conditions ensuring global exponential stability of the unique equilibrium of the neural network. Numerical simulations are conducted to illustrate the effectiveness of the derived conditions in characterizing stability regions of the neural network