Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913807245246464 |
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| author | Mazurek, Szymon Caputa, Jakub Argasiński, Jan K. Wielgosz, Maciej |
| author_facet | Mazurek, Szymon Caputa, Jakub Argasiński, Jan K. Wielgosz, Maciej |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_05341 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective Mazurek, Szymon Caputa, Jakub Argasiński, Jan K. Wielgosz, Maciej Neural and Evolutionary Computing Artificial Intelligence Machine Learning 92B20, 68T05, 92B25, 37N25, 60J22, 68Q32 I.2.6; I.2.10; I.2.9; I.2.3; C.1.3; F.4.1; J.2 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. |
| title | Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective |
| topic | Neural and Evolutionary Computing Artificial Intelligence Machine Learning 92B20, 68T05, 92B25, 37N25, 60J22, 68Q32 I.2.6; I.2.10; I.2.9; I.2.3; C.1.3; F.4.1; J.2 |
| url | https://arxiv.org/abs/2504.05341 |