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Temperature dependence of phase and spike synchronization of neural networks

Author

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  • Budzinski, R.C.
  • Boaretto, B.R.R.
  • Prado, T.L.
  • Lopes, S.R.

Abstract

We simulate a small-world neural network composed of 2000 thermally sensitive identical Hodgkin–Huxley type neurons investigating the synchronization characteristics as a function of the coupling strength and the temperature of the neurons. The Kuramoto order parameter computed over individual neuron membrane potential signals, and recurrence analysis evaluated from the mean field of the network are used to identify the non-monotonous behavior of the synchronization level as a function of the coupling parameter. We show that moderated high temperatures induce a low variability of the inter-burst intervals of neurons leading to phase synchronization and further increases of temperature result in a low variability of inter-spike intervals leading the network to display spike synchronization.

Suggested Citation

  • Budzinski, R.C. & Boaretto, B.R.R. & Prado, T.L. & Lopes, S.R., 2019. "Temperature dependence of phase and spike synchronization of neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 123(C), pages 35-42.
    • Handle: RePEc:eee:chsofr:v:123:y:2019:i:c:p:35-42
    DOI: 10.1016/j.chaos.2019.03.039
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    References listed on IDEAS

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    1. Budzinski, R.C. & Boaretto, B.R.R. & Rossi, K.L. & Prado, T.L. & Kurths, J. & Lopes, S.R., 2018. "Nonstationary transition to phase synchronization of neural networks induced by the coupling architecture," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 321-334.
    2. Erick Olivares & Simón Salgado & Jean Paul Maidana & Gaspar Herrera & Matías Campos & Rodolfo Madrid & Patricio Orio, 2015. "TRPM8-Dependent Dynamic Response in a Mathematical Model of Cold Thermoreceptor," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-17, October.
    3. M. E. J. Newman & D. J. Watts, 1999. "Scaling and Percolation in the Small-World Network Model," Working Papers 99-05-034, Santa Fe Institute.
    4. Boaretto, B.R.R. & Budzinski, R.C. & Prado, T.L. & Kurths, J. & Lopes, S.R., 2018. "Suppression of anomalous synchronization and nonstationary behavior of neural network under small-world topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 126-138.
    5. Etémé, Armand S. & Tabi, Conrad B. & Mohamadou, Alidou, 2017. "Synchronized nonlinear patterns in electrically coupled Hindmarsh–Rose neural networks with long-range diffusive interactions," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 813-826.
    6. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
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    Cited by:

    1. Ge, Mengyan & Lu, Lulu & Xu, Ying & Mamatimin, Rozihajim & Pei, Qiming & Jia, Ya, 2020. "Vibrational mono-/bi-resonance and wave propagation in FitzHugh–Nagumo neural systems under electromagnetic induction," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).

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