REGULATING THE NONLINEAR DYNAMICS OF OLFACTORY CORTEX

The dynamic behaviour of cortical structures can be changed significan tly in character by different types of neuromodulators. We simulate su ch effects in a neural network model of the olfactory cortex and analy se the resulting nonlinear dynamics of this system, including during b oth learning and recall. The model has simple network units and realis tic network connectivity. 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 paramete r can be thought of as corresponding to the concentration of a particu lar neuromodulator in the system. It can also be related to 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 non-chaotic attractor behaviour. We also display 'transien t chaos' phenomena, which begin with chaos-like behaviour but eventual ly converge to a limit cycle. We demonstrate that the complex dynamics under neuromodulatory control can enhance weak signals and reduce rec all time considerably, in particular when going from point attractor t o limit cycle dynamics. Finally, we discuss the biological significanc e of these findings, recognizing the difficulties in characterizing th e nonlinear dynamical states involved.