Noise and neuromodulatory effects in a cortical associative memory model

We study the role of complex neurodynamics in learning and associative memory using a neural network model of the olfactory cortex. By varying the noise level and a control parameter, corresponding to the level of neuromodulator or arousal, we analyze the resulting nonlinear dynamics during learning and recall of constant and oscillatory input. Point attractor, limit cycle, and strange attractor dynamics occur at different values of the control parameter. We show that oscillations and chaos-like behavior can give shorter recall times and more robust memory states than in static cases. In particular, we show that the recall time can reach a minimum for additive and multiplicative noise. Also noise-induced state transitions and noise-induced chaos-like behavior is demonstrated