Simulated neuromodulatory effects on the nonlinear dynamics of olfactory cortex

The dynamic behavior of cortical structures can change significantly in character by different types of neuromodulators. We simulate such effects in a neural network model of the olfactory cortex and analyze the resulting nonlinear dynamics of this system. The model uses simple network units and a network connectivity which closely resembles that of the real cortex. The input-output relation of populations of neurons is represented as a sigmoid function, with a single parameter determining threshold, slope and amplitude of the curve. This parameter is taken to correspond to the level of neuromodulator and correlated with the level of arousal of an animal. By varying this "gain parameter" we show that the model can give point attractor, limit cycle attractor and strange chaotic or nonchaotic attractor behavior. We also display "transient chaos", which begins with chaos-like behavior but eventually goes to a limit cycle. We show how this complex dynamics is related to learning and associative memory in our system and discuss the biological significance of this.