STDP helps neurons learn from the timing of spikes.
STDP 帮助神经元从放电的时间关系中学习。
In computational neuroscience, spike-timing-dependent plasticity is often used to model how neural circuits adapt through experience.
在计算神经科学中,尖峰时间依赖性可塑性常用于建模神经回路如何通过经验而适应与改变。
Bi, G.-Q., & Poo, M.-M. (1998), Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type(经典实验论文,系统展示与“spike timing”相关的突触改变规律)。
Markram, H., Lübke, J., Frotscher, M., & Sakmann, B. (1997), Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs(早期关键工作之一,提出时序相关的突触效能调节思想)。
Dayan, P., & Abbott, L. F. (2001), Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems(教材/专著中常以 STDP 作为学习与可塑性的重要模型)。
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014), Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition(专著中讨论 STDP 及其在网络学习中的作用)。