mlsanityData to 14 Oct 2026

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

arXiv 2504.05341 ↗First posted 6 Apr 2025cs.NE cs.AI cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 26 of its 130 references that are in the map. First posted on arXiv 6 Apr 2025.

Builds on

26 of 130 references.

References · 26

  1. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 752
  2. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
  3. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
  4. 2019The Heidelberg spiking datasets for the systematic evaluation of spiking neural networkscited by 137
  5. 2021Efficient Neuromorphic Signal Processing with Loihi 2cited by 96
  6. 2016Towards deep learning with segregated dendritescited by 71
  7. 2018Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rulescited by 67
  8. 2020Meta-Learning through Hebbian Plasticity in Random Networkscited by 47
  9. 2022ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescalescited by 35
  10. 2018Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networkscited by 27
  11. 2023Training Full Spike Neural Networks via Auxiliary Accumulation Pathwaycited by 26
  12. 2024SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learningcited by 25
14 more
  1. 2019Biologically inspired alternatives to backpropagation through time for learning in recurrent neural netscited by 20
  2. 2017First-spike based visual categorization using reward-modulated STDPcited by 19
  3. 2018Introducing Neuromodulation in Deep Neural Networks to Learn Adaptive Behaviourscited by 16
  4. 2019Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networkscited by 13
  5. 2022Neuromorphic Artificial Intelligence Systemscited by 12
  6. 2020A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinascited by 10
  7. 2023ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardwarecited by 10
  8. 2019Cortical credit assignment by Hebbian, neuromodulatory and inhibitory plasticitycited by 8
  9. 2022Contextual guidance: An integrated theory for astrocytes function in brain circuits and behaviorcited by 6
  10. 2023Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networkscited by 5
  11. 2023Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networkscited by 3
  12. 2024Uncontrolled learning: co-design of neuromorphic hardware topology for neuromorphic algorithmscited by 3
  13. 2021Homeostatic Criticality in Neuronal Networkscited by 1
  14. 2023Bio-realistic Neural Network Implementation on Loihi 2 with Izhikevich Neuronscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Compression and Inference of Spiking Neural Networks on Resource-Constrained Hardwarecited by 0
  2. 2025Energy-Efficient Eimeria Parasite Detection Using a Two-Stage Spiking Neural Network Architecturecited by 0
  3. 2026NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Frameworkcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control
  2. 2024P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks
  3. 2025SpikeFit: Towards Optimal Deployment of Spiking Networks on Neuromorphic Hardware
  4. 2025Adaptive Surrogate Gradients for Sequential Reinforcement Learning in Spiking Neural Networks
  5. 2024Learning fast changing slow in spiking neural networks
  6. 2023High-performance deep spiking neural networks with 0.3 spikes per neuron
  7. 2024CoLaNET -- A Spiking Neural Network with Columnar Layered Architecture for Classification
  8. 2025A PyTorch-Compatible Spike Encoding Framework for Energy-Efficient Neuromorphic Applications