用三因素学习提升脉冲神经网络的自适应能力
Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective
- 引入神经调质信号改进传统学习机制
- 增强信用分配能力，提升学习效率
- 适合研究类脑智能与神经形态计算的人士
脉冲神经网络中的三因素学习规则作为传统赫布学习和尖峰时间依赖可塑性（STDP）的重要扩展，通过引入神经调质信号，提升了系统的自适应性和学习效率。该机制增强了生物合理性，并改善了人工神经系统中的信用分配问题。本文从机器学习视角综述了三因素学习的最新进展，探讨其理论基础、算法实现及其在强化学习与神经形态计算中的应用价值。同时，分析了跨学科方法、可扩展性挑战以及在机器人、认知建模和人工智能系统中的潜在应用。最后，指出了关键研究空白，并提出未来推动神经科学与人工智能融合的方向。
原文摘要 · Abstract (English)
Three-factor learning rules in Spiking Neural Networks (SNNs) have emerged as a crucial extension to traditional Hebbian learning and Spike-Timing-Dependent Plasticity (STDP), incorporating neuromodulatory signals to improve adaptation and learning efficiency. These mechanisms enhance biological plausibility and facilitate improved credit assignment in artificial neural systems. This paper takes a view on this topic from a machine learning perspective, providing an overview of recent advances in three-factor learning, discusses theoretical foundations, algorithmic implementations, and their relevance to reinforcement learning and neuromorphic computing. In addition, we explore interdisciplinary approaches, scalability challenges, and potential applications in robotics, cognitive modeling, and AI systems. Finally, we highlight key research gaps and propose future directions for bridging the gap between neuroscience and artificial intelligence.
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