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Electromagnetic stimulation is an important neuromodulation technique that modulates the electrical activity of neurons and affects cortical excitability for the purpose of modulating the nervous system. The phenomenon of inverse stochastic resonance is a response mechanism of the biological nervous system to external signals and plays an important role in the signal processing of the nervous system. In this paper, a small-world neural network with electrical synaptic connections was constructed, and the inverse stochastic resonance of the small-world neural network under electromagnetic stimulation was investigated by analyzing the dynamics of the neural network. The results showed that: the Levy channel noise under electromagnetic stimulation could cause the occurrence of inverse stochastic resonance in small-world neural networks; the characteristic index and location parameter of the noise had significant effects on the intensity and duration of the inverse stochastic resonance in neural networks; the larger the probability of randomly adding edges and the number of nearest neighbor nodes in small-world networks, the more favorable the anti-stochastic resonance was; by adjusting the electromagnetic stimulation parameters, a dual regulation of the inverse stochastic resonance of the neural network can be achieved. The results of this study provide some theoretical support for exploring the regulation mechanism of electromagnetic nerve stimulation technology and the signal processing mechanism of nervous system.

This paper primarily investigates the inverse stochastic resonance (ISR) of neuron network driven by Lévy noise with electrical autapse and chemical autapse, respectively. Firstly, the discharge of Hodgkin–Huxley (HH) neuron network under different noise parameters, autapse parameters and network coupling strength is shown. Then, the variation of average firing rate with Lévy noise in the case of electrical autapse and chemical autapse is presented. We find that there exists a minimum value of the average firing rate curve caused by stability index and noise intensity of Lévy noise across the whole network, which is the phenomenon of ISR. With the increase of autaptic intensity and coupling strength, the ISR inhibitory effect of neuron discharge is weakened. In addition, with the increase of coupling strength, the neuron discharge of neural network is more intense and regular. As a consequence, our work suggests that autaptic intensity and coupling efficient of neuronal network can regulate the neuronal firing activities and suppress the effect of ISR, and Lévy noise can induce ISR phenomenon in Newman–Watts neuronal network. [ABSTRACT FROM AUTHOR]

In this paper, the phenomenon of inverse stochastic resonance (ISR) induced by Levy noise is investigated in a single neuron. Firstly, the discharge of Hodgkin–Huxley neuron at different current strengths without noise is studied. Then we cite the concept of average spiking rate and use it as a scale to measure the discharge of neuron. Under the influence of Levy noise, it is found that the average firing rate can reach a minimum caused by stability index and a scale parameter of Levy noise. The conclusion is that ISR could be induced by Levy noise when the input current strength reaches the critical threshold. Besides, in order to further prove that Levy noise can produce ISR, interspike intervals (ISIs) histogram is used for studying discharge patterns in voltage. Finally, we find that with the increase of stability index and scale parameter of Levy noise, the total number of ISIs decreases first and then increases, which implies the appearance of ISR. [ABSTRACT FROM AUTHOR]

• Two-compartment neuron model is used to simulate the real morphology of neurons. • Effects of both neuronal morphology and time delay on inverse stochastic resonance phenomenon are studied. • Neuronal morphology influences number of stable states and inverse stochastic resonance (ISR) phenomenon. • ISR effect occurs within the bistable state region of parameters space. • Multiple ISR phenomenon is induced by time delay. Inverse stochastic resonance (ISR) is a depression phenomenon of firing activity of neuron with respect to noise, and the characteristics of neural systems are determined by neuronal morphology. The effects of neuronal morphology on ISR remain unknown. Here, ISR effect in two-compartment neuron model is investigated. It is found that the neuronal morphology influences the number of stable states of neuronal discharge, thereby governs ISR effect. The ISR effect appears within the parameters region of bistable state, which is consistent with prior experimental and theoretical findings. With the increasing of time delay, average firing rate of neuron exhibits multiple local minima, which is known as multiple ISR. Time delay and coupling strength affect neuronal firing pattern by altering phase of stimulus and strength of autaptic current. These results may provide a novel perspective on ISR effect in nervous system. [ABSTRACT FROM AUTHOR]

Abstract: In order to explain the occurrence of a minimum in firing rate which occurs for certain mean input levels as noise level increases (inverse stochastic resonance, ISR) in Hodgkin–Huxley (HH) systems, we analyze the underlying transitions from a stable equilibrium point to limit cycle and vice-versa. For a value of at which ISR is pronounced, properties of the corresponding stable equilibrium point are found. A linearized approximation around this point has oscillatory solutions from whose maxima spikes tend to occur. A one dimensional diffusion is also constructed for small noise. Properties of the basin of attraction of the limit cycle (spike) are investigated heuristically. Long term trials of duration 500000 ms are carried out for values of from 0 to 2.0. The graph of mean spike count versus is divided into 4 regions , where contains the minimum associated with ISR. In transitions to the basin of attraction of the rest point are not observed until a small critical value of is reached, at the beginning of . The sudden decline in firing rate when is just greater than implies that there is only a small range of noise levels where repetitive spiking is safe from annihilation by noise. The firing rate remains small throughout . At a larger critical value which signals the beginning of , the probability of transitions from the basin of attraction of the equilibrium point to that of the limit cycle apparently becomes greater than zero and the spike rate thereafter increases with increasing . The quantitative scheme underlying the ISR curve is outlined in terms of the properties of exit time random variables. In the final subsection, several statistical properties of the main random variables associated with long term spiking activity are given, including distributions of exit times from the two relevant basins of attraction and the interspike interval. [Copyright &y& Elsevier]