Effects of Spike Frequency Adaptation on Synchronization Transitions in Electrically Coupled Neuronal Networks with Scale-Free Connectivity  

作　　者：汪雷 张溥明 梁培基 邱意弘 

机构地区：[1]School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240

出　　处：《Chinese Physics Letters》2014年第7期32-35,共4页中国物理快报（英文版）

基　　金：Supported by Shanghai Jiao Tong University Fund for Interdisciplinary Research for Medical Applications under Grant No YG2013MS28, and the National Natural Science Foundation of China under Grant Nos 61075108 and 61375114.

摘　　要：Effects of spike frequency adaptation （SFA ） on the synchronous behavior of population neurons are investigated in electrically coupled networks with a scale-free property. By a computational approach, we corroborate that pairwise correlations between neurons would decrease if neurons exhibit the feature of SFA, which is similar to previous experimental observations. However, unlike the case of pairwise correlations, population activities of neurons show a rather complex variation mode： compared with those of non-adapted neurons, neurons in the networks having weak-degrees of SFA will impair population synchronizations; while neurons exhibiting strong- degrees of SFA will enhance population synchronizations. Moreover, a variation of coupling strength between neurons will not alter this phenomenon significantly, unless the coupling strength is too weak. Our results suggest that synchronous activity of electrically coupled population neurons is adaptation-dependent, and this adaptive feature may imply some coding strategies of neuronal populations.Effects of spike frequency adaptation （SFA ） on the synchronous behavior of population neurons are investigated in electrically coupled networks with a scale-free property. By a computational approach, we corroborate that pairwise correlations between neurons would decrease if neurons exhibit the feature of SFA, which is similar to previous experimental observations. However, unlike the case of pairwise correlations, population activities of neurons show a rather complex variation mode： compared with those of non-adapted neurons, neurons in the networks having weak-degrees of SFA will impair population synchronizations; while neurons exhibiting strong- degrees of SFA will enhance population synchronizations. Moreover, a variation of coupling strength between neurons will not alter this phenomenon significantly, unless the coupling strength is too weak. Our results suggest that synchronous activity of electrically coupled population neurons is adaptation-dependent, and this adaptive feature may imply some coding strategies of neuronal populations.

分 类 号：TP335.4[自动化与计算机技术—计算机系统结构] TU457[自动化与计算机技术—计算机科学与技术]