Gong Yubing, Xu Bo, Xu Qiang, Yang Chuanlu, Ren Tingqi, Hou Zhonghuai, Xin Houwen
Department of Physics, Yantai Normal University, Yantai, Shandong 264025, People's Republic of China.
Phys Rev E Stat Nonlin Soft Matter Phys. 2006 Apr;73(4 Pt 2):046137. doi: 10.1103/PhysRevE.73.046137. Epub 2006 Apr 28.
We have studied the effect of random long-range connections in chaotic thermosensitive neuron networks with each neuron being capable of exhibiting diverse bursting behaviors, and found stochastic synchronization and optimal spatiotemporal patterns. For a given coupling strength, the chaotic burst-firings of the neurons become more and more synchronized as the number of random connections (or randomness) is increased and, rather, the most pronounced spatiotemporal pattern appears for an optimal randomness. As the coupling strength is increased, the optimal randomness shifts towards a smaller strength. This result shows that random long-range connections can tame the chaos in the neural networks and make the neurons more effectively reach synchronization. Since the model studied can be used to account for hypothalamic neurons of dogfish, catfish, etc., this result may reflect the significant role of random connections in transferring biological information.
我们研究了随机长程连接在混沌热敏神经元网络中的作用，其中每个神经元都能够表现出多种爆发行为，并发现了随机同步和最优时空模式。对于给定的耦合强度，随着随机连接数（或随机性）的增加，神经元的混沌爆发放电越来越同步，相反，在最优随机性时会出现最显著的时空模式。随着耦合强度的增加，最优随机性向较小强度偏移。这一结果表明，随机长程连接可以抑制神经网络中的混沌，使神经元更有效地实现同步。由于所研究的模型可用于解释角鲨、鲶鱼等的下丘脑神经元，这一结果可能反映了随机连接在传递生物信息中的重要作用。
