Coherence resonance in stimulated neuronal network

Andrey V. Andreev, Vladimir V. Makarov, Anastasija E. Runnova, Alexander N. Pisarchik and Alexander E. Hramov

Chaos, Solitons & Fractals, 2018, vol. 106, issue C, 80-85

Abstract: We consider a neuronal network model where an external stimulus excites some neurons, which in turn activate other neurons in the network via synapse. We find that the regularity in macroscopic spiking activity of the whole neuronal network maximizes at a certain level of intrinsic noise. A similar resonant behavior, referred to as coherence resonance, is also observed with respect to the stimulus strength, network size, and number of stimulated neurons. The coherence is quantitatively estimated with the signal-to-noise ratio calculated from the average power spectra of the macroscopic signal and with autocorrelation time. Overall synchronization in the neuronal network also exhibits a non-monotonic dependence on the network size.

Keywords: Neural oscillator; Map; Coherence; Synchronization; Complex network; Neuronal network (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:106:y:2018:i:c:p:80-85

DOI: 10.1016/j.chaos.2017.11.017

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