Self-exciting chaos as a dynamic model for irregular neural spiking

We introduce a nonlinear dynamical system with self-exciting chaotic dynami cs. Its interspike interval return mag shows a noisy Poisson-like distribut ion. Spike sequences from different initial conditions are unrelated but po ssess the same mean frequency. In the presence of noisy perturbations, sequ ences started from different initial conditions synchronize. The features o f the model are compared with experimental results for irregular spike sequ ences in neurons. Self-exciting chaos offers a mechanism for temporal codin g of complex input signals.