Scientific Results

Experimental study of firing death in a network of chaotic FitzHugh-Nagumo neurons

Year: 2013

Authors: Ciszak M., Euzzor S., Arecchi F. T., Meucci R.

Autors Affiliation: CNR-Istituto Nazionale di Ottica, Largo E. Fermi 6, 50125 Florence, Italy; Department of Physics, University of Florence, Florence, Italy

Abstract: The FitzHugh-Nagumo neurons driven by a periodic forcing undergo a period-doubling route to chaos and a transition to mixed-mode oscillations. When coupled, their dynamics tend to be synchronized. We show that the chaotically spiking neurons change their internal dynamics to subthreshold oscillations, the phenomenon referred to as firing death. These dynamical changes are observed below the critical coupling strength at which the transition to full chaotic synchronization occurs. Moreover, we find various dynamical regimes in the subthreshold oscillations, namely, regular, quasiperiodic, and chaotic states. We show numerically that these dynamical states may coexist with large-amplitude spiking regimes and that this coexistence is characterized by riddled basins of attraction. The reported results are obtained for neurons implemented in the electronic circuits as well as for the model equations. Finally, we comment on the possible scenarios where the coupling-induced firing death could play an important role in biological systems.


Volume: 87 (2)      Pages from: 022919  to: 022919

More Information: M.C. and R. M. wish to thank Regione Toscana, S. Mancuso, and F. Pavone for financial support. The work was partly supported by Ente Cassa di Risparmio di Firenze.
KeyWords: A transitions; Chaotic state; Chaotic synchronization; Critical coupling; Dynamical regime; Dynamical state; Experimental studies; Fitzhugh-nagumo neurons; Internal dynamics; Mixed mode oscillations; Model equations; Period-doubling; Periodic forcing; Quasi-periodic; Riddled basins; Spiking neuron; Subthreshold oscillations, Chaotic systems; Dynamics; Neurons, Neural networks, action potential; animal; article; biological model; biological rhythm; computer simulation; human; nerve cell; nerve cell network; nonlinear system; physiology, Action Potentials; Animals; Biological Clocks; Computer Simulation; Humans; Models, Neurological; Nerve Net; Neurons; Nonlinear Dynamics
DOI: 10.1103/PhysRevE.87.022919

Citations: 15
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