Spiking activities in chain neural network driven by channel noise with field coupling

The distribution of electromagnetic field in both intracellular and extracellular environments can be changed by fluctuations in the membrane potential, and the effects of electromagnetic induction should be considered in dealing with neuronal electrical activities, wherein field coupling plays a very important role in signal exchange between neurons. In this paper, basing on an improved electromagnetic induction model, a chain network is designed to investigate the responses of the neural system to channel noise under field coupling. Both the synchronization factor and coefficient of variation are numerically simulated, and it is found that (i) the weak field coupling strength is conducive to the regularity of discharge patterns in the neuronal network; (ii) the synchronization of neural spikes can be enhanced by selecting a suitable coupling intensity; and (iii) in the presence of the weak noise intensity, the discharge mode of neuron is easily affected by the inducing coefficient. Our results show that the regularity of discharge patterns in a stochastic neural network depends on the field coupling intensity, which reflects the importance of field coupling in the selection of neural discharge modes.