Effects of magnetic fields on stochastic resonance in Hodgkin-Huxley neuronal network driven by Gaussian noise and non-Gaussian noise

Random fluctuations and synaptic plasticity enhance working memory activities in the neuron-astrocyte network

Random fluctuations are inescapable feature in biological systems, but appropriate intensity of randomness can effectively facilitate information transfer and memory encoding within the nervous system. In the study, a modified spiking neuron-astrocyte network model with excitatory-inhibitory balance and synaptic plasticity is established. This model considers external input noise, and allows investigating the effects of intrinsic random fluctuations on working memory tasks. It is found that the astrocyte network, acting as a low-pass filter, reduces the noise component of the total input currents and improves the recovered images. The memory performance is enhanced by selecting appropriate intensity of random fluctuations, while excessive intensity can inhibit signal transmission of network. As the intensity of random fluctuations gradually increases, there exists a maximum value of the working memory performance. The cued recall of the network markedly decreases excessive input noise relative to test images. Meanwhile, a greater contrast effect is observed as the external input noise increases. In addition, synaptic plasticity reduces the firing rates and firing peaks of neurons, thus stabilizing the working memory activity during the test. The outcomes of this study may provide some inspirations for comprehending the role of random fluctuations in working memory mechanisms and neural information processing within the cerebral cortex.

Controlling spatiotemporal dynamics of neural networks by Lévy noise

We explore numerically how additive Lévy noise influences the spatiotemporal dynamics of a neural network of nonlocally coupled FitzHugh-Nagumo oscillators. Without noise, the network can exhibit various partial or cluster synchronization patterns, such as chimera and solitary states, which can also coexist in the network for certain values of the control parameters. Our studies show that these structures demonstrate different responses to additive Lévy noise and, thus, the dynamics of the neural network can be effectively controlled by varying the scale parameter and the stability index of Lévy noise. Specifically, introducing Lévy noise in the multistability mode can increase the probability of observing chimera states while suppressing solitary states. Nonetheless, decreasing the stability parameter enables one to diminish the noise effect on chimera states and amplify it on solitary states.

Detecting stochastic multiresonance in neural networks via statistical complexity measure

This paper employs statistical complexity measure (SCM) to investigate the occurrence of stochastic multiresonance (SMR) induced by noise and time delay in small-world neural networks coupled with FitzHugh-Nagumo (FHN) neurons. Our findings reveal that SCM exhibits four local maxima at four optimal noise levels, providing evidence for the occurrence of quadruple stochastic resonances. When time delay τ is taken into account in the information transmission, under moderate noise levels, SCM shows several local maxima when τ = n T e with n being a positive integer and T e being the period of subthreshold signal. This indicates the appearance of delay-induced SMR at the multiples of the period of subthreshold signal. Intriguingly, at low noise levels, a strong coherence between time delay and neuronal firing dynamics emerges at τ = n T e - 2 , as confirmed by a series of SCM maxima at these time delays. Furthermore, the study demonstrates that by adjusting the degrees and sizes of small-world networks, as well as the coupling strength, it is possible to optimize the strength of delay-induced SMR, thus maximizing the detection capability of subthreshold signal. The research results may provide us with an effective approach for understanding the role of time delay in signal detection and information transmission.

A Novel Piecewise Tri-Stable Stochastic Resonance System Driven by Dichotomous Noise

Stochastic resonance (SR) has been widely studied as a means of signal processing since its conception. Since SR is different from other denoising methods in nature, it can be used for not only feature extraction but also signal enhancement. Additive white Gaussian noise (AWGN) is often used as a driving source in SR research due to its convenience in numerical simulation and uniform distribution, but as a special noise, it is of great significance to study the SR principle of dichotomous noise as a driving source for nonlinear dynamics. In this paper, the method of piecewise tri-stable SR (PTSR) driven by dichotomous noise is studied, and it is verified that signal enhancement can still be achieved in the PTSR system. At the same time, the influence of the parameters of the PTSR system, periodic signal, and dichotomous noise on the mean of signal-to-noise ratio gain (SNR-GM) is analyzed. Finally, dichotomous noise and AWGN are used as the driving sources of the PTSR system, and the signal enhancement ability and noise resistance ability of the two drivers are compared.