Chaos synchronization in gap-junction-coupled neurons.

Depending on temperature, the modified Hodgkin-Huxley (MHH) equations exhibit a variety of dynamical behaviors, including intrinsic chaotic firing. We analyze synchronization in a large ensemble of MHH neurons that are interconnected with gap junctions. By evaluating tangential Lyapunov exponents we clarify whether the synchronous state of neurons is chaotic or periodic. Then, we evaluate transversal Lyapunov exponents to elucidate if this synchronous state is stable against infinitesimal perturbations. Our analysis elucidates that with weak gap junctions, the stability of the synchronization of MHH neurons shows rather complicated changes with temperature. We, however, find that with strong gap junctions, the synchronous state is stable over the wide range of temperature irrespective of whether synchronous state is chaotic or periodic. It turns out that strong gap junctions realize the robust synchronization mechanism, which well explains synchronization in interneurons in the real nervous system.

[1]  Golomb,et al.  Dynamics of globally coupled inhibitory neurons with heterogeneity. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[2]  G. Buzsáki,et al.  Gamma Oscillation by Synaptic Inhibition in a Hippocampal Interneuronal Network Model , 1996, The Journal of Neuroscience.

[3]  M. Yoshioka Linear stability analysis of retrieval state in associative memory neural networks of spiking neurons. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Masahiko Yoshioka Cluster synchronization in an ensemble of neurons interacting through chemical synapses. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Kapitaniak,et al.  Different types of chaos synchronization in two coupled piecewise linear maps. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[6]  Monika Sharma,et al.  Chemical oscillations , 2006 .

[7]  G. Ermentrout,et al.  Gamma rhythms and beta rhythms have different synchronization properties. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[8]  G. Ermentrout,et al.  Multiple pulse interactions and averaging in systems of coupled neural oscillators , 1991 .

[9]  A. Pikovsky,et al.  Resolving clusters in chaotic ensembles of globally coupled identical oscillators. , 2001, Physical review letters.

[10]  H. Fujisaka,et al.  Stability Theory of Synchronized Motion in Coupled-Oscillator Systems , 1983 .

[11]  G. Buzsáki,et al.  High-frequency network oscillation in the hippocampus. , 1992, Science.

[12]  D. Perkel,et al.  Quantitative methods for predicting neuronal behavior , 1981, Neuroscience.

[13]  I. Shimada,et al.  A Numerical Approach to Ergodic Problem of Dissipative Dynamical Systems , 1979 .

[14]  Germán Mato,et al.  Synchrony in Excitatory Neural Networks , 1995, Neural Computation.

[15]  J. Milnor On the concept of attractor , 1985 .

[16]  Carson C. Chow,et al.  Frequency Control in Synchronized Networks of Inhibitory Neurons , 1998, Journal of Computational Neuroscience.

[17]  R. Traub,et al.  Synchronized oscillations in interneuron networks driven by metabotropic glutamate receptor activation , 1995, Nature.

[18]  Martin Tobias Huber,et al.  Computer Simulations of Neuronal Signal Transduction: The Role of Nonlinear Dynamics and Noise , 1998 .

[19]  Miles A Whittington,et al.  Interneuron Diversity series: Inhibitory interneurons and network oscillations in vitro , 2003, Trends in Neurosciences.

[20]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[21]  Kunihiko Kaneko,et al.  Information cascade with marginal stability in a network of chaotic elements , 1994 .

[22]  Yoshiki Kuramoto,et al.  Chemical Oscillations, Waves, and Turbulence , 1984, Springer Series in Synergetics.

[23]  Frank Moss,et al.  Homoclinic bifurcation in a Hodgkin-Huxley model of thermally sensitive neurons. , 2000, Chaos.