Refractoriness in Poisson and Gaussian first-order neural nets with chemical markers
In this work first order probabilistic Poisson and Gaussian neural nets with chemical markers are investigated, analytically and by computer simulations. The investigation of steady-state behavior of these systems is extended here to systems in which the refractory period is assigned to be 1 for all or some of the subpopulations of the net, whereas the remainder are characterized by zero refractory periods. The interest is focused on the effects of refractoriness on the neural activities. Results obtained show the existence of several critical points at high initial activities, which are a consequence of the nonzero refractory periods. For these points a larger initial activity, above a certain critical level, results in the reduction of activity to a lower stable steady-state, instead of the highest one. We also find that in the Gaussian nets each critical point is lower than the corresponding one as in the Poisson nets. Finally, a discussion of the results is made.
refractoriness neural nets chemical markers