Adaptive targeting of chaotic response in periodically stimulated neural systems

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...

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Bibliographic Details
Published inChaos (Woodbury, N.Y.) Vol. 16; no. 2; p. 023116
Main Authors Gupta, Kopal, Singh, Harinder P, Biswal, B, Ramaswamy, R
Format Journal Article
LanguageEnglish
Published United States 01.06.2006
Subjects
Action Potentials - physiology
Adaptation, Physiological - physiology
Animals
Biological Clocks - physiology
Computer Simulation
Electric Stimulation - methods
Humans
Models, Neurological
Models, Statistical
Nerve Net - physiology
Neuronal Plasticity - physiology
Neurons - physiology
Nonlinear Dynamics
Periodicity
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Summary: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.
ISSN:1054-1500
DOI:10.1063/1.2204749