Ruan Yanhua, Zhao Gang
Institute of Complex Bio-dynamics, Jiangxi Blue Sky University, Nanchang, Jiangxi 330098, China.
Neural Plast. 2009;2009:704075. doi: 10.1155/2009/704075. Epub 2009 Jul 16.
We discuss effects of various experimentally supported STDP learning rules on frequency synchronization of two unidirectional coupled neurons systematically. First, we show that synchronization windows for all STDP rules cannot be enhanced compared to constant connection under the same model. Then, we explore the influence of learning parameters on synchronization window and find optimal parameters that lead to the widest window. Our findings indicate that synchronization strongly depends on the specific shape and the parameters of the STDP update rules. Thus, we give some explanations by analyzing the synchronization mechanisms for various STDP rules finally.
我们系统地讨论了各种经实验支持的STDP学习规则对两个单向耦合神经元频率同步的影响。首先,我们表明,在相同模型下,与恒定连接相比,所有STDP规则的同步窗口都无法得到增强。然后,我们探讨了学习参数对同步窗口的影响,并找到了导致最宽窗口的最优参数。我们的研究结果表明,同步强烈依赖于STDP更新规则的具体形状和参数。因此,我们最后通过分析各种STDP规则的同步机制给出了一些解释。