A neural model of hippocampal-parietal interactions in spatial memory

Excitable dynamics of a neuron generates dynamical phenomena with various attractors, which include deterministic chaos and multistability [1, 2]. A neural network composed of neurons with such excitable dynamics also produces much more interesting and richer nonlinear dynamics with spatio– temporal structure of spikes [3, 4]. In this talk, I consider nonlinear dynamics with spatio–temporal spikes and possible information coding. In particular, a possibility of spatio–temporal coding based upon inter–event intervals is explored from the viewpoint of nonlinear time series analysis [5], where the event can be a single spike, more robust synchronous firing and so on. Effects of noise on such spatio–temporal coding are also discussed.