title: Dynamical phases of the Hindmarsh-Rose neuronal model: studies of the transition from bursting to spiking chaos.
abstract: The dynamical phases of the Hindmarsh-Rose neuronal model are analyzed in detail by varying the external current I. For increasing current values, the model exhibits a peculiar cascade of nonchaotic and chaotic period-adding bifurcations leading the system from the silent regime to a chaotic state dominated by bursting events. At higher I-values, this phase is substituted by a regime of continuous chaotic spiking and finally via an inverse period doubling cascade the system returns to silence. The analysis is focused on the transition between the two chaotic phases displayed by the model: one dominated by spiking dynamics and the other by bursts. At the transition an abrupt shrinking of the attractor size associated with a sharp peak in the maximal Lyapunov exponent is observable. However, the transition appears to be continuous and smoothed out over a finite current interval, where bursts and spikes coexist. The beginning of the transition (from the bursting side) is signaled from a structural modification in the interspike interval return map. This change in the map shape is associated with the disappearance of the family of solutions responsible for the onset of the bursting chaos. The successive passage from bursting to spiking chaos is associated with a progressive pruning of unstable long-lasting bursts.
authors.affiliation: Dipartimento di Sistemi ed Informatica, Universitá di Firenze, via S. Marta 3, 50139 Firenze, Italy. giacomo.innocenti@gmail.com
authors.forename: Giacomo
authors.initials: G
authors.lastname: Innocenti
authors.forename: Alice
authors.initials: A
authors.lastname: Morelli
authors.forename: Roberto
authors.initials: R
authors.lastname: Genesio
authors.forename: Alessandro
authors.initials: A
authors.lastname: Torcini
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D008962:Models, Theoretical; D009474:Neurons / Q000473:pathology*; D017711:Nonlinear Dynamics; D009435:Synaptic Transmission; D013598:Systems Theory; D013997:Time Factors