Noise-enhanced performance in a cortical associative memory model.

Spontaneous neuronal activity and synaptic noise are well-known phenomena, but their biological significance has not yet been assessed. Using a computer model of the olfactory cortex we show that such activity, expressed as temporal noise in the model, can reduce recall time in associative memory tasks. We investigate both additive and multiplicative noise, and find optimal noise levels for which the recall time reaches a minimum. In addition, we demonstrate that noise can induce state transitions, such that the system is pushed from one attractor state to another. For high enough noise levels the dynamics can change dramatically and, for example, switch from an oscillatory to a chaos-like behavior. We discuss these findings in light of their significance for neural information processing.