Comparison of single neuron models in terms of synchronization propensity.

A plausible model for coherent perception is the synchronization of chaotically distributed neural spike trains over wide cortical areas. A recently introduced propensity criterion provides a tool for a quantitative comparison of different neuron models in terms of their ability to synchronize to an applied perturbation. We explore the propensity of several systems and indicate the requirements to be satisfied by a plausible candidate for modeling neuronal activity. Our results show that the conflicting requirements of stability and sensitivity leading to high propensity to synchronization can be satisfied by a strongly nonuniform attractor made of two distinct regions: a saddle focus plus a sufficiently separated saddle node.

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

[2]  T. Sejnowski,et al.  Synchronous oscillatory activity in sensory systems: new vistas on mechanisms , 1997, Current Opinion in Neurobiology.

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

[4]  G. Laurent,et al.  Who reads temporal information contained across synchronized and oscillatory spike trains? , 1998, Nature.

[5]  S Boccaletti,et al.  Competition of synchronization domains in arrays of chaotic homoclinic systems. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  F T Arecchi,et al.  Synchronization of homoclinic chaos. , 2001, Physical review letters.

[7]  S Boccaletti,et al.  Noise-enhanced synchronization of homoclinic chaos in a CO2 laser. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  W. Singer,et al.  The gamma cycle , 2007, Trends in Neurosciences.

[9]  John Rinzel,et al.  A Formal Classification of Bursting Mechanisms in Excitable Systems , 1987 .

[10]  Alex M. Andrew,et al.  Spiking Neuron Models: Single Neurons, Populations, Plasticity , 2003 .

[11]  R. FitzHugh Impulses and Physiological States in Theoretical Models of Nerve Membrane. , 1961, Biophysical journal.

[12]  James P. Keener,et al.  Mathematical physiology , 1998 .

[13]  I Leyva,et al.  Propensity criterion for networking in an array of coupled chaotic systems. , 2003, Physical review letters.

[14]  R. Rosenfeld Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[15]  P Colet,et al.  Theory of collective firing induced by noise or diversity in excitable media. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Eugene M. Izhikevich,et al.  “Subcritical Elliptic Bursting of Bautin Type ” (Izhikevich (2000b)). The following , 2022 .

[17]  Frank Moss,et al.  Low-Dimensional Dynamics in Sensory Biology 1: Thermally Sensitive Electroreceptors of the Catfish , 1997, Journal of Computational Neuroscience.

[18]  Pablo Varona,et al.  Neural Signatures: Multiple Coding in Spiking–bursting Cells , 2006, Biological Cybernetics.

[19]  Francisco Rodríguez-Quioñes Spanish researchers defeated by the system , 1999, Nature.

[20]  Meucci,et al.  Laser dynamics with competing instabilities. , 1987, Physical review letters.

[21]  S. Boccaletti,et al.  Synchronization of chaotic systems , 2001 .

[22]  Mingzhou Ding,et al.  Transitions to synchrony in coupled bursting neurons. , 2004, Physical review letters.

[23]  Jürgen Kurths,et al.  Noise-induced synchronization and coherence resonance of a Hodgkin-Huxley model of thermally sensitive neurons. , 2003, Chaos.

[24]  A. Selverston,et al.  Dynamical principles in neuroscience , 2006 .

[25]  Antonino Raffone,et al.  Dynamic synchronization and chaos in an associative neural network with multiple active memories. , 2003, Chaos.

[26]  Xiao-Jing Wang,et al.  Genesis of bursting oscillations in the Hindmarsh-Rose model and homoclinicity to a chaotic saddle , 1993 .

[27]  S Boccaletti,et al.  Information encoding in homoclinic chaotic systems. , 2001, Chaos.

[28]  Christoph von der Malsburg,et al.  The Correlation Theory of Brain Function , 1994 .

[29]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[30]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[31]  Stephen Grossberg,et al.  Introduction: Spiking Neurons in Neuroscience and Technology , 2001, Neural Networks.

[32]  B. Bunney,et al.  Firing properties of substantia nigra dopaminergic neurons in freely moving rats. , 1985, Life sciences.

[33]  S Boccaletti,et al.  In phase and antiphase synchronization of coupled homoclinic chaotic oscillators. , 2004, Chaos.

[34]  Spike synchronization of a chaotic array as a phase transition , 2007, 0709.1108.

[35]  Eugene M. Izhikevich,et al.  Neural excitability, Spiking and bursting , 2000, Int. J. Bifurc. Chaos.

[36]  K. Schäfer,et al.  Oscillation and noise determine signal transduction in shark multimodal sensory cells , 1994, Nature.

[37]  Wulfram Gerstner,et al.  SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .

[38]  Alexander N. Pisarchik,et al.  Theoretical and experimental study of discrete behavior of Shilnikov chaos in a CO2 laser , 2001 .