Effect of weighting parameters on dynamical behavior of Hopfield neural networks with logistic map activation functions

Abstract

In ANN terminology, the synaptic connections are the weights of the neural networks and can be seen as an interaction between neurons. In this paper, we consider two simple neurons which have both self-coupling and non-invertible activation functions. Our studies on these interactions lead to different dynamical behaviors of the network. We show that they can be used as a means of chaos generation or suppression to neuron's outputs when more adaptability or stability is required. This idea may be further used for chaos synchronization of neuron's outputs.