title: Self-exciting chaos as a dynamic model for irregular neural spiking
abstract: We introduce a nonlinear dynamical system with self-exciting chaotic dynamics. Its interspike interval return map shows a noisy Poisson-like distribution. Spike sequences from different initial conditions are unrelated but possess the same mean frequency. In the presence of noisy perturbations, sequences started from different initial conditions synchronize. The features of the model are compared with experimental results for irregular spike sequences in neurons. Self-exciting chaos offers a mechanism for temporal coding of complex input signals.
authors.affiliation: Facultad de Ciencias, Universidad Autonoma del Estado de Morelos Col. Chamilpa, 62210 Cuernavaca, Morelos, Mexico.
authors.initials: G
authors.lastname: Baier
authors.lastname: Escalera Santos GJ
authors.initials: H
authors.lastname: Perales
authors.initials: M
authors.lastname: Rivera
authors.initials: M
authors.lastname: Muller
authors.initials: R
authors.lastname: Leder
authors.initials: P
authors.lastname: Parmananda