A Simple Chaotic Neuron Model : Stochastic Behavior of Neural Networks

Abstract

We have shortly reviewed the occurrence of the post-synaptic potentials between neurons, the relation between EEG and neuron dynamics, as well as methods of signal analysis. We supposed a simple stochastic model representing electrical activity of neuronal systems. The model is constructed using the Monte Carlo simulation technique. The results yielded EEG-like signals with their phase portraits in three-dimensional space. The Lyapunov exponent was positive, indicating a chaotic behavior. The correlation dimension of the EEG-like signals was found to be .92, which was smaller than those reported by others. It was concluded that this neuron model may provide valuable clues about the dynamic behavior of neural systems.