论文标题
在距离依赖性耦合下，神经网络中的同步可延展性
Synchronization malleability in neural networks under a distance-dependent coupling
论文作者
论文摘要
我们研究了幂律模型的距离依赖性耦合下的尖峰神经元网络的同步特征。拓扑和耦合强度之间的相互作用导致存在不同的时空模式，对应于非同步或相同步状态。特别有趣的是我们所说的同步延展性，其中系统描绘了由于神经输入的不同顺序，因此相同参数的相位同步度显着不同。我们通过计算神经元尖峰列车之间的相互信息来分析网络的功能连接性，从而使我们能够表征网络中同步的结构。我们表明，这些结构取决于网络呈现同步延展性的参数区域的输入排序，我们建议这是由于耦合，连接体系结构和单个神经输入之间的复杂相互作用所致。
We investigate the synchronization features of a network of spiking neurons under a distance-dependent coupling following a power-law model. The interplay between topology and coupling strength leads to the existence of different spatiotemporal patterns, corresponding to either non-synchronized or phase-synchronized states. Particularly interesting is what we call synchronization malleability, in which the system depicts significantly different phase synchronization degrees for the same parameters as a consequence of a different ordering of neural inputs. We analyze the functional connectivity of the network by calculating the mutual information between neuronal spike trains, allowing us to characterize the structures of synchronization in the network. We show that these structures are dependent on the ordering of the inputs for the parameter regions where the network presents synchronization malleability and we suggest that this is due to a complex interplay between coupling, connection architecture, and individual neural inputs.
