突触记忆巩固与智能可塑性理论：面向持续学习
Theories of synaptic memory consolidation and intelligent plasticity for continual learning
AI总结：
本章综述了基于突触内部状态演化和智能调控单个突触可塑性的元可塑性理论，阐明其对实现神经网络持续学习的关键作用，并总结了在深度神经网络中的应用及未来研究方向。
摘要：
人类和动物在一生中不断学习，这种持续学习能力对智能至关重要。在本章中，我们考察了具有复杂内部突触动态的可塑性机制在使神经网络具备这种能力方面可能发挥的关键作用。通过综述理论研究，我们强调持续学习的两个基本使能因素。第一，突触可塑性机制必须在多个与行为相关的时间尺度上维持并演化内部状态。第二，可塑性算法必须利用内部状态智能地调控单个突触的可塑性，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在维持持续学习能力方面的重要意义。最后，我们概述了进一步研究的方向，以理解大脑卓越的持续学习能力，并为人工智能系统利用类似机制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
