The efficiency of the chaotic visual behaviour in modeling the human perceptual-alternation by artificial neural network

Some issues are investigated concerning representations of perceptual-alternation phenomena in the multilayer neural network based on the Riani et al. model, where the alternation is interpreted as that of the firing-rate for the pattern perceived. In particular, our results of computer simulations have shown the chaotic behaviour of visual inputs corresponding to attention eye movement makes it possible to obtain clearer Gamma distributions of the experimental perceptual duration of the alternating interpretations than those of the random visual behaviour. We also discuss some other efficiencies of the chaotic visual inputs, for example, such as the input pattern-dependence of the firing-rate.