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.

翻译：人类和动物在整个生命周期中持续学习。这种持续学习对智能发展至关重要。在本章中，我们探讨了具有复杂内部突触动态的可塑性机制在神经网络中实现这种能力的关键作用。通过梳理理论研究，我们揭示了持续学习的两个基本实现要素：首先，突触可塑性机制必须在多个行为相关时间尺度上维持并演化内部状态；其次，可塑性算法必须利用内部状态智能调节单个突触的可塑性，以促进新记忆的无缝整合，同时避免对已有记忆产生有害干扰。本章涵盖这些原理在深度神经网络中的成功应用，并强调突触元可塑性对维持持续学习能力的重要意义。最后，我们展望了未来研究方向，以理解大脑卓越的持续学习能力，并为人工智能系统开发类似机制。