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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Conditional Lyapunov exponents and transfer entropy in coupled bursting neurons under excitation and coupling mismatch

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Author(s):
Soriano, Diogo C. [1, 2] ; dos Santos, Odair V. [3] ; Suyama, Ricardo [1, 2] ; Fazanaro, Filipe I. [1] ; Attux, Romis [2, 4]
Total Authors: 5
Affiliation:
[1] Fed Univ ABC, Ctr Engn Modeling & Appl Social Sci CECS, Santo Andre - Brazil
[2] Brazilian Inst Neurosci & Neurotechnol BRAINN, Campinas, SP - Brazil
[3] Fed Univ Tocantins, Arraias, TO - Brazil
[4] Univ Estadual Campinas, UNICAMP, Sch Elect & Comp Engn FEEC, Dept Comp Engn & Ind Automat DCA, Campinas, SP - Brazil
Total Affiliations: 4
Document type: Journal article
Source: COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION; v. 56, p. 419-433, MAR 2018.
Web of Science Citations: 1
Abstract

This work has a twofold aim: (a) to analyze an alternative approach for computing the conditional Lyapunov exponent (lambda(cmax)) aiming to evaluate the synchronization stability between nonlinear oscillators without solving the classical variational equations for the synchronization error dynamical system. In this first framework, an analytic reference value for lambda(cmax) is also provided in the context of Duffing master-slave scenario and precisely evaluated by the proposed numerical approach; (b) to apply this technique to the study of synchronization stability in chaotic Hindmarsh-Rose (HR) neuronal models under uni- and bi-directional resistive coupling and different excitation bias, which also considered the root mean square synchronization error, information theoretic measures and asymmetric transfer entropy in order to offer a better insight of the synchronization phenomenon. In particular, statistical and information theoretical measures were able to capture similarity increase between the neuronal oscillators just after a critical coupling value in accordance to the largest conditional Lyapunov exponent behavior. On the other hand, transfer entropy was able to detect neuronal emitter influence even in a weak coupling condition, i.e. under the increase of conditional Lyapunov exponent and apparently desynchronization tendency. In the performed set of numerical simulations, the synchronization measures were also evaluated for a two-dimensional parameter space defined by the neuronal coupling (emitter to a receiver neuron) and the (receiver) excitation current. Such analysis is repeated for different feedback couplings as well for different (emitter) excitation currents, revealing interesting characteristics of the attained synchronization region and conditions that facilitate the emergence of the synchronous behavior. These results provide a more detailed numerical insight of the underlying behavior of a HR in the excitation and coupling space, being in accordance with some general findings concerning HR coupling topologies. As a perspective, besides the synchronization overview from different standpoints, we hope that the proposed numerical approach for conditional Lyapunov exponent evaluation could outline a valuable strategy for studying neuronal stability, especially when realistic models are considered, in which analytical or even Jacobian evaluation could define a laborious or impracticable task. (C) 2017 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology
Grantee:Fernando Cendes
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC