A Gaussian Approach to Neural Nets with Multiple Memory Domains

Non-isola ted randomly interconnected neural nets with chemical markers are investigated, which receive steady or slowly varying excitatory or inhibitory inputs. We extend here our previous studies to include nets of Poisson and Gaussian connectivities. Our results show that the multi-hysteresis loops obtained by applying the steady-state condition for the Gaussian approximation are wider than the corresponding ones of the Poisson case, and they have been slightly shifted to larger values of the parameter s 1 (which is the fraction of external active ® bres). Also, in the Gaussian nets, the stable steady states are lower than the corresponding ones of the Poisson nets, whereas the unstable states are higher.