Stochastic resonance in hybrid scale-free neuronal networks

Ergin Yilmaz, Muhammet Uzuntarla, Mahmut Ozer and Matjaž Perc

Physica A: Statistical Mechanics and its Applications, 2013, vol. 392, issue 22, 5735-5741

Abstract: We study the phenomenon of stochastic resonance in a system of coupled neurons that are globally excited by a weak periodic input signal. We make the realistic assumption that the chemical and electrical synapses interact in the same neuronal network, hence constituting a hybrid network. By considering a hybrid coupling scheme embedded in the scale-free topology, we show that the electrical synapses are more efficient than chemical synapses in promoting the best correlation between the weak input signal and the response of the system. We also demonstrate that the average degree of neurons within the hybrid scale-free network significantly influences the optimal amount of noise for the occurrence of stochastic resonance, indicating that there also exists an optimal topology for the amplification of the response to the weak input signal. Lastly, we verify that the presented results are robust to variations of the system size.

Keywords: Hybrid synapses; Stochastic resonance; Scale-free network (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:392:y:2013:i:22:p:5735-5741

DOI: 10.1016/j.physa.2013.07.011

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