Stochastic resonance can induce oscillation in a recurrent Hodgkin-Huxley neuron model with added Gaussian noise

Author(s):  
Hiroyuki Mino ◽  
Dominique M. Durand
2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Alessandra Paffi ◽  
Francesca Camera ◽  
Chiara Carocci ◽  
Francesca Apollonio ◽  
Micaela Liberti

Tinnitus is a debilitating perception of sound in the absence of external auditory stimuli. It may have either a central or a peripheral origin in the cochlea. Experimental studies evidenced that an electrical stimulation of peripheral auditory fibers may alleviate symptoms but the underlying mechanisms are still unknown. In this work, a stochastic neuron model is used, that mimics an auditory fiber affected by tinnitus, to check the effects, in terms of firing reduction, of different kinds of electric stimulations, i.e., continuous wave signals and white Gaussian noise. Results show that both white Gaussian noise and continuous waves at tens of kHz induce a neuronal firing reduction; however, for the same amplitude of fluctuations, Gaussian noise is more efficient than continuous waves. When contemporary applied, signal and noise exhibit a cooperative effect in retrieving neuronal firing to physiological values. These results are a proof of concept that a combination of signal and noise could be delivered through cochlear prosthesis for tinnitus suppression.


1996 ◽  
Vol 53 (1) ◽  
pp. 1273-1275 ◽  
Author(s):  
François Chapeau-Blondeau ◽  
Xavier Godivier ◽  
Nicolas Chambet

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Peiming Shi ◽  
Pei Li ◽  
Shujun An ◽  
Dongying Han

Stochastic resonance (SR) is investigated in a multistable system driven by Gaussian white noise. Using adiabatic elimination theory and three-state theory, the signal-to-noise ratio (SNR) is derived. We find the effects of the noise intensity and the resonance system parametersb,c, anddon the SNR; the results show that SNR is a nonmonotonic function of the noise intensity; therefore, a multistable SR is found in this system, and the value of the peak changes with changing the system parameters.


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