Many mathematical models of synaptic plasticity have been proposed to explain the diversity of plasticity phenomena observed in biological organisms. These models range from simple interpretations of Hebb's postulate, which suggests that correlated neural activity leads to increases in synaptic strength, to more complex rules that allow bidirectional synaptic updates, ensure stability, or incorporate additional signals like reward or error. At the same time, a range of learning paradigms can be observed behaviorally, from Pavlovian conditioning to motor learning and memory recall. Although it is difficult to directly link synaptic updates to learning outcomes experimentally, computational models provide a valuable tool for building evidence of this connection. In this chapter, we discuss several fundamental learning paradigms, along with the synaptic plasticity rules that might be used to implement them.

翻译：许多突触可塑性的数学模型被提出来解释在生物有机体中观察到的多样性可塑性现象。这些模型的范围从对赫布假定的简单解释——该假说认为相关的神经活动会导致突触强度增强——到更复杂的规则，这些规则允许双向突触更新、确保稳定性，或整合如奖励或误差等额外信号。同时，从行为层面可以观察到一系列学习范式，从巴甫洛夫条件反射到运动学习和记忆提取。尽管在实验上很难直接将突触更新与学习结果联系起来，但计算模型为构建这种联系的证据提供了有价值的工具。在本章中，我们讨论了几种基本的学习范式，以及可能用于实现它们的突触可塑性规则。