Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective
This is an incremental overview paper that synthesizes existing research on three-factor learning in SNNs for researchers in machine learning, neuroscience, and AI.
This paper provides an overview of three-factor learning rules in Spiking Neural Networks, which extend traditional Hebbian learning and STDP by incorporating neuromodulatory signals to improve adaptation and learning efficiency, and discusses their theoretical foundations, algorithmic implementations, and applications in reinforcement learning and neuromorphic computing.
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.