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神经元网络中依赖距离的时滞引起的锁定。

Locking induced by distance-dependent delay in neuronal networks.

机构信息

State Key Laboratory of Mechanics and Control of Mechanical Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

出版信息

Phys Rev E. 2016 Nov;94(5-1):052405. doi: 10.1103/PhysRevE.94.052405. Epub 2016 Nov 14.

Abstract

In the present paper, the locking phenomenon induced by distance-dependent delay in ring structured neuronal networks is investigated, wherein each neuron is modeled by a FitzHugh-Nagumo neuron. Through increasing the element time delay, the different spatiotemporal patterns are observed. By calculating the interspike interval and its value that is divided by the delay of the nearest neurons, it is found that these patterns are actually the lockings between the period of spiking and the distance-dependent delay of the connected neurons. The lockings could also be revealed by the mean time lag of the neurons and in different connection topologies. Furthermore, the influences of the network size and the coupling strength are investigated, wherein the former seems to play a negligible role on these locking patterns; in contrast, too small coupling strengths will blur the boundaries of different patterns and too large ones may destroy the high ratio locking patterns. Finally, one may predict the locking order which determines the emergence order of the patterns in the networks.

摘要

在本文中,研究了环形结构神经元网络中时滞相关延迟引起的锁定现象,其中每个神经元由 FitzHugh-Nagumo 神经元建模。通过增加元件时滞,可以观察到不同的时空模式。通过计算峰间间隔及其除以最近神经元延迟的值,可以发现这些模式实际上是神经元发放周期与连接神经元的时滞相关延迟之间的锁定。通过神经元的平均时滞和不同的连接拓扑也可以揭示锁定。此外,还研究了网络规模和耦合强度的影响,其中前者似乎对这些锁定模式的影响可以忽略不计;相反,过小的耦合强度会模糊不同模式的边界,而过大的耦合强度可能会破坏高比例锁定模式。最后,可以预测锁定顺序,这决定了网络中模式的出现顺序。

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