Stationary log-normal distribution of weights stems from spontaneous ordering in adaptive node networks.

ABSTRACT: Experimental evidence recently indicated that neural networks can learn in a different manner than was previously assumed, using adaptive nodes instead of adaptive links. Consequently, links to a node undergo the same adaptation, resulting in cooperative nonlinear dynamics with oscillating effective link weights. Here we show that the biological reality of stationary log-normal distribution of effective link weights in neural networks is a result of such adaptive nodes, although each effective link weight varies significantly in time. The underlying mechanism is a stochastic restoring force emerging from a spontaneous temporal ordering of spike pairs, generated by strong effective link preceding by a weak one. In addition, for feedforward adaptive node networks the number of dynamical attr

SUBMITTER: Uzan H 

PROVIDER: S-EPMC6117314 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature