Analysis of chaos phenomena in strong nonlinear synapse neural networks
Algis Garliauskas · 1999
The analysis of a chaos theory, in general, and a neural network chaos paradigm with a specific interpretation by a simple neural network, in particular, allowed us to set up a new aspect in an artificial neuronal approach: an analogy between natural chaos experimentally observed in neural systems of the brain and artificial neural network chaos phenomena has been considered. The significance of asymmetry and nonlinearity, which were increased on introducing a restricted N-shaped synapse relation in a dynamic model, is emphasized. There are illustrated the different computational examples of the neural network properties which are expressed by the equilibrium point, stable cycle or chaotic behaviour in strong nonlinear neural systems.