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

arXiv: https://arxiv.org/abs/2504.05341 · HTML: https://mlsanity.com/paper/2504.05341/

## 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 (most cited first, 26 of 26)

- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks](https://mlsanity.com/paper/1910.07407/) (2019)
- [Efficient Neuromorphic Signal Processing with Loihi 2](https://mlsanity.com/paper/2111.03746/) (2021)
- [Towards deep learning with segregated dendrites](https://mlsanity.com/paper/1610.00161/) (2016)
- [Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules](https://mlsanity.com/paper/1801.05219/) (2018)
- [Meta-Learning through Hebbian Plasticity in Random Networks](https://mlsanity.com/paper/2007.02686/) (2020)
- [ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales](https://mlsanity.com/paper/2208.09759/) (2022)
- [Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks](https://mlsanity.com/paper/1804.00227/) (2018)
- [Training Full Spike Neural Networks via Auxiliary Accumulation Pathway](https://mlsanity.com/paper/2301.11929/) (2023)
- [SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning](https://mlsanity.com/paper/2401.04491/) (2024)
- [Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets](https://mlsanity.com/paper/1901.09049/) (2019)
- [First-spike based visual categorization using reward-modulated STDP](https://mlsanity.com/paper/1705.09132/) (2017)
- [Introducing Neuromodulation in Deep Neural Networks to Learn Adaptive Behaviours](https://mlsanity.com/paper/1812.09113/) (2018)
- [Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks](https://mlsanity.com/paper/1902.03187/) (2019)
- [Neuromorphic Artificial Intelligence Systems](https://mlsanity.com/paper/2205.13037/) (2022)
- [A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas](https://mlsanity.com/paper/2005.06318/) (2020)
- [ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware](https://mlsanity.com/paper/2301.08281/) (2023)
- [Cortical credit assignment by Hebbian, neuromodulatory and inhibitory plasticity](https://mlsanity.com/paper/1911.00307/) (2019)
- [Contextual guidance: An integrated theory for astrocytes function in brain circuits and behavior](https://mlsanity.com/paper/2211.09906/) (2022)
- [Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks](https://mlsanity.com/paper/2307.06084/) (2023)
- [Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks](https://mlsanity.com/paper/2306.01906/) (2023)
- [Uncontrolled learning: co-design of neuromorphic hardware topology for neuromorphic algorithms](https://mlsanity.com/paper/2408.05183/) (2024)
- [Homeostatic Criticality in Neuronal Networks](https://mlsanity.com/paper/2109.02718/) (2021)
- [Bio-realistic Neural Network Implementation on Loihi 2 with Izhikevich Neurons](https://mlsanity.com/paper/2307.11844/) (2023)

## Built on by (most cited first, 3 of 3)

- [Compression and Inference of Spiking Neural Networks on Resource-Constrained Hardware](https://mlsanity.com/paper/2511.12136/) (2025)
- [Energy-Efficient Eimeria Parasite Detection Using a Two-Stage Spiking Neural Network Architecture](https://mlsanity.com/paper/2601.00806/) (2025)
- [NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework](https://mlsanity.com/paper/2605.15058/) (2026)

## 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.