Humans and animals learn throughout life. Such continual learning is crucial
for intelligence. In this chapter, we examine the pivotal role plasticity
mechanisms with complex internal synaptic dynamics could play in enabling this
ability in neural networks. By surveying theoretical research, we highlight two
fundamental enablers for continual learning. First, synaptic plasticity
mechanisms must maintain and evolve an internal state over several behaviorally
relevant timescales. Second, plasticity algorithms must leverage the internal
state to intelligently regulate plasticity at individual synapses to facilitate
the seamless integration of new memories while avoiding detrimental
interference with existing ones. Our chapter covers successful applications of
these principles to deep neural networks and underscores the significance of
synaptic metaplasticity in sustaining continual learning capabilities. Finally,
we outline avenues for further research to understand the brain's superb
continual learning abilities and harness similar mechanisms for artificial
intelligence systems.

人和动物终身学习，这对智能至关重要。我们在本章中研究了复杂内部突触动力学的可塑性机制在实现神经网络的终身学习能力中所起的关键作用。通过调查理论研究，我们强调了促进终身学习的两个基本因素：一是突触可塑性机制必须在多个行为相关的时间尺度上保持和演化内部状态；二是可塑性算法必须利用内部状态智能地调节单个突触的可塑性，以便实现对新记忆的无缝整合，并避免对现有记忆的干扰。我们的章节涵盖了这些原理在深度神经网络中的成功应用，并强调了突触元可塑性在维持终身学习能力方面的重要性。最后，我们概述了进一步研究的方向，以了解大脑卓越的终身学习能力，并利用类似机制来开发人工智能系统。