Effect Of Channel Block On Dynamical Behavior Of Adaptive Neuronal Network

In this paper,we study the effect of channel block on the dynamic behavior of adaptive neuronal networks.The article choose an adaptive neural network which with spike-timing-dependent plasticity(STDP),study the effect of ion channel noise and channel block on the coherence and synchronization of neuronal firing activities in different network structures.Because channel block can affect the number of working ion channels in neural cells,and the intensity of channel noise is related to the number of ion channels,we first study the effect of channel noise on the dynamic behavior of the neural network with different network structures.It is find that:1.In scale-free(SF)stochastic Hodgkin-Huxley(HH)neuronal networks with time delays,the temporal coherence of the neuronal networks changes as channel noise intensity is varied in different ways depending on the range of channel noise intensity.The temporal coherence monotonically decreases with the increase of channel noise intensity for too small or too big channel noise intensity.However,for intermediate channel noise intensity as channel noise intensity is varied,exhibiting coherence resonance(CR).Moreover,this phenomenon is dependent on coupling strength and network average degree and becomes strongest when they are optimal.This result shows that channel noise can enhance the temporal coherence of delayed neural networks by inducing CR.2.In adaptive SF stochastic HH neuronal networks with time delays,the channel noise intensity can enhance the synchronization transitions(ST)induced by time delay.The ST change is most obvious when the channel noise intensity is optimal.This phenomenon depends on STDP and network average degree,and it can be either enhanced or suppressed as network average degree increases depending on channel noise intensity.These results show that there are optimal channel noise and network average degree that can enhance the ST by time delay in the adaptive neuronal networks.These findings could help us to better understand the regulation of channel noise on the synchronization of adaptive neural networks.3.In adaptive Newman-Watts(NW)small-world stochastic HH neuron networks,STDP can either enhance or suppress the CR induced by channel noise when the adjusting rate of STDP decreases or increases.STDP can alter the effects of network randomness and network size on the CR induced by channel noise.Under STDP,for electrical coupling there are optimal network randomness and network size by which the intrinsic CR becomes strongest,however,for chemical coupling the intrinsic CR is always enhanced as network randomness or network size increases,which are different from the results for fixed coupling.These results show that the CR induced by channel noise of the neuronal networks can be either enhanced or suppressed by STDP,and there are optimal network randomness and network size by which the intrinsic CR becomes strongest.These findings could help us to further understand the regulatory effect of STDP on the coherence of adaptive neural networks under channel noise.Finally,We study effect of channel block on the dynamic behavior of adaptive neuronal networks.It is found that:In adaptive SF stochastic HH neuronal networks with time delays,the effect of channel block on multiple CR induced by time delay varies with the intensity of channel noise,and appropriate channel noise intensity makes multiple CR phenomenon more significant.K~+ channel block suppresses multiple CR induced by time delay,but Na~+ channel block can enhance multiple CR induced by time delay,and there is optimal Na~+ channel block level by which multiple CR becomes most pronounced.In addition,STDP has a significant influence on the effect of channel block on multiple CR.As adjusting rate of STDP increases,for K~+ channel block there is proper adjusting rate by which multiple CR is most pronounced;however,for Na~+ channel block multiple CR is reduced.These findings could provide a new insight into effect of channel block on information processing in neural systems.