mlsanityData to 14 Oct 2026

S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks

arXiv 2306.15220 ↗First posted 27 Jun 2023cs.NE cs.LGcited by 7

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Abstract (arXiv)

Spiking Neural Networks (SNNs) are biologically plausible models that have been identified as potentially apt for deploying energy-efficient intelligence at the edge, particularly for sequential learning tasks. However, training of SNNs poses significant challenges due to the necessity for precise temporal and spatial credit assignment. Back-propagation through time (BPTT) algorithm, whilst the most widely used method for addressing these issues, incurs high computational cost due to its temporal dependency. In this work, we propose S-TLLR, a novel three-factor temporal local learning rule inspired by the Spike-Timing Dependent Plasticity (STDP) mechanism, aimed at training deep SNNs on event-based learning tasks. Furthermore, S-TLLR is designed to have low memory and time complexities, which are independent of the number of time steps, rendering it suitable for online learning on low-power edge devices. To demonstrate the scalability of our proposed method, we have conducted extensive evaluations on event-based datasets spanning a wide range of applications, such as image and gesture recognition, audio classification, and optical flow estimation. In all the experiments, S-TLLR achieved high accuracy, comparable to BPTT, with a reduction in memory between 5-50× and multiply-accumulate (MAC) operations between 1.3-6.6×.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 35 of its 63 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

35 of 63 references.

References · 35

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
  4. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 406
  5. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 345
  6. 2021Deep Residual Learning in Spiking Neural Networkscited by 331
  7. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
  8. 2018Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotioncited by 307
  9. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 278
  10. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 266
  11. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 259
  12. 2018The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perceptioncited by 254
23 more
  1. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 245
  2. 2016Direct Feedback Alignment Provides Learning in Deep Neural Networkscited by 240
  3. 2018EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Camerascited by 224
  4. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 198
  5. 2019The Heidelberg spiking datasets for the systematic evaluation of spiking neural networkscited by 137
  6. 2020Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancycited by 125
  7. 2021Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networkscited by 99
  8. 2020Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networkscited by 98
  9. 2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
  10. 2018Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rulescited by 67
  11. 2022Online Training Through Time for Spiking Neural Networkscited by 49
  12. 2018A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rulecited by 36
  13. 2020Online Spatio-Temporal Learning in Deep Neural Networkscited by 32
  14. 2021Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learningcited by 27
  15. 2017BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticitycited by 23
  16. 2022Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamicscited by 19
  17. 2023ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardwarecited by 10
  18. 2023Online Spatio-Temporal Learning with Target Projectioncited by 9
  19. 2023Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networkscited by 5
  20. 2022Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognitioncited by 4
  21. 2022An STDP-Based Supervised Learning Algorithm for Spiking Neural Networkscited by 3
  22. 2022Hardware/Software co-design with ADC-Less In-memory Computing Hardware for Spiking Neural Networkscited by 3
  23. 2022A Taxonomy of Recurrent Learning Rulescited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 1.

7 papers.

  1. 2025TESS: A Scalable Temporally and Spatially Local Learning Rule for Spiking Neural Networkscited by 4
  2. 2025EgoEvGesture: Gesture Recognition Based on Egocentric Event Cameracited by 4
  3. 2025Traces Propagation: Memory-Efficient and Scalable Forward-Only Learning in Spiking Neural Networkscited by 1
  4. 2025ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimationcited by 0
  5. 2025Learning with Spike Synchrony in Spiking Neural Networkscited by 0
  6. 2025Hyperdimensional Decoding of Spiking Neural Networkscited by 0
  7. 2026NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Frameworkcited by 0