Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective

2025-04-06

AI Summary

This study addresses three key challenges in spiking neural networks (SNNs): difficult credit assignment, weak biological plausibility, and low learning efficiency. To this end, we propose a systematic optimization framework grounded in triphasic learning—integrating presynaptic and postsynaptic spikes with neuromodulatory signals. For the first time, we unify the triphasic mechanism from a machine learning perspective, incorporating extended spike-timing-dependent plasticity (STDP), reward-modulated STDP (R-STDP), multi-timescale neuromodulation, and spike-train optimization, while jointly coupling reinforcement learning strategies with brain-inspired hardware mapping techniques. We innovatively establish a scalable evaluation criterion to rigorously characterize the performance boundaries of mainstream SNN algorithms. Experiments on robotic control and few-shot sequential learning tasks demonstrate that our approach improves generalization accuracy by 12–23% over conventional SNNs, significantly enhancing cognitive interpretability and fostering synergistic advancement between artificial intelligence and neuroscience.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Social networks and social learning
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Extending Hebbian learning with neuromodulatory signals in SNNs
• Improving credit assignment in artificial neural systems
• Bridging neuroscience and AI via three-factor learning

Innovation

• Three-factor learning rules enhance SNN adaptation
• Neuromodulatory signals improve learning efficiency
• Interdisciplinary approaches bridge neuroscience and AI