Guo Daqing, Li Chunguang
School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
IEEE Trans Neural Netw. 2010 Jun;21(6):895-905. doi: 10.1109/TNN.2010.2044419. Epub 2010 Apr 12.
In this paper, we study the self-sustained irregular firing activity in 2-D small-world (SW) neural networks consisting of both excitatory and inhibitory neurons by computational modeling. For a proper proportion of unidirectional shortcuts, the stable self-sustained activity with irregular firing states indeed occurs in the considered network. By varying the shortcut density while keeping other system parameters fixed, different levels of irregular firing states, from weakly irregular to Poisson-like and burst firing states, are obtained in 2-D SW neural networks. It is also observed that this activity is sensitive to small perturbations, which might provide a possible mechanism for producing chaos. On the other hand, we find that several other system parameters, such as the network size and refractory period, have significant impact on this activity. Further simulation results show that the 2-D SW neural network can sustain such long-lasting firing behavior by using a smaller number of connections than the random neural network.
在本文中，我们通过计算建模研究了由兴奋性和抑制性神经元组成的二维小世界（SW）神经网络中的自持不规则放电活动。对于适当比例的单向捷径，在所考虑的网络中确实会出现具有不规则放电状态的稳定自持活动。在保持其他系统参数不变的情况下，通过改变捷径密度，在二维SW神经网络中获得了从弱不规则到类泊松和爆发放电状态的不同程度的不规则放电状态。还观察到这种活动对小扰动敏感，这可能为产生混沌提供一种可能的机制。另一方面，我们发现其他几个系统参数，如网络大小和不应期，对这种活动有显著影响。进一步的模拟结果表明，二维SW神经网络通过使用比随机神经网络更少的连接数就能维持这种持久的放电行为。
