Excitation Properties of the Biological Neurons with Side-Inhibition Mechanism in Small-World Networks

We have studied the excitation properties of biophysical Hodgkin-Huxley neurons with the side-inhibition mechanism in small-world networks. The result shows that the excitation properties in the networks are preferably consistent with the characteristic properties of a brain neural system under external constant stimuli, such as fatigue effect, extreme excitation principle, and the brain neural excitation response induced by different in- tensity of noise and coupling. The results of the study might shed some light on the study of the brain nerve electrophysiology and epistemological science.     

CrI_3高压相变及其光学性质理论研究

利用第一性原理计算方法,探讨了体相CrI_3在低温斜方六面体结构(■,BiI_3-type)及高压单斜结构(C2/m,AlCl_3-type)的相变、电子结构和光学性质.计算结果显示,半导体CrI_3当压强增加到26.1GPa时,高压导致的晶格畸变致使CrI_3从相■变化到相C2/m;原子之间的错位位移,使导带处的能带发生下移,价带处的能带发生了一定程度的上移,导致带隙减小.两种相的光学性质进一步验证了这些特性.     

小世界神经网络的二次超谐波随机共振

随机共振现象是非线性系统中普遍存在的自然现象.主要通过研究小世界生物神经网络中的输出信噪比与反映小世界效应的重连概率p、耦合强度c以及输入信号振幅A之间的关系，来揭示小世界生物神经网络的二次超谐波随机共振的一些规律.发现对于Hodgkin-Huxley小世界神经网络，并不是信号越强，信噪比越大，而是输入信号的振幅A存在一个最优值A_O，此时网络信噪比最大.     

Stochastic Resonance in Neural Systems with Small-World Connections

We study the stochastic resonance （SR） in Hodgkin-Huxley （HH） neural systems with small-world （SW） connections under the noise synaptic current and periodic stimulus, focusing on the dependence of properties of SR on coupling strength c. It is found that there exists a critical coupling strength c^＊ such that if c 〈 c^＊, then the SR can appear on the SW neural network. Especially, dependence of the critical coupling strength c^＊ on the number of neurons N shows the monotonic even almost linear increase of c^＊ as N increases and c^＊ on the SW network is smaller than that on the random network. For the effect of the SW network on the phenomenon of SR, we show that decreasing the connection-rewiring probability p of the network topology leads to an enhancement of SR. This indicates that the SR on the SW network is more prominent than that on the random network （p = 1.0）. In addition, it is noted that the effect becomes remarkable as coupling strength increases. Moreover, it is found that the SR weakens but resonance range becomes wider with the increase of c on the SW neural network.