Temperature dependence of phase and spike synchronization of neural networks

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

Chaos, Solitons & Fractals, 2019, vol. 123, issue C, 35-42

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.

Keywords: Neural network; Synchronization; Nonlinear dynamics, (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:123:y:2019:i:c:p:35-42

DOI: 10.1016/j.chaos.2019.03.039

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