title: Adaptive targeting of chaotic response in periodically stimulated neural systems.
abstract: We demonstrate a technique for the enhancement of chaos in a computational model of a periodically stimulated excitable neuron. "Anticontrol" of chaos is achieved through intermittent adaptive intervention, which is based on finite-time Lyapunov exponents measured from the time series. Our results suggest that an adaptive strategy for chaos anticontrol is viable for increasing the complexity in physiological systems that are typically both noisy and nonstationary.
authors.affiliation: Department of Physics and Astrophysics, University of Delhi, Delhi 110007, India.
authors.forename: Kopal
authors.initials: K
authors.lastname: Gupta
authors.forename: Harinder P
authors.initials: HP
authors.lastname: Singh
authors.forename: B
authors.initials: B
authors.lastname: Biswal
authors.forename: R
authors.initials: R
authors.lastname: Ramaswamy
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000222:Adaptation, Physiological / Q000502:physiology; D000818:Animals; D001683:Biological Clocks / Q000502:physiology*; D003198:Computer Simulation; D004558:Electric Stimulation / Q000379:methods; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D010507:Periodicity