mlsanityData to 7 Oct 2026

Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks

arXiv 1804.00227 ↗First posted 31 Mar 2018cs.CV q-bio.NCcited by 25

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Abstract (arXiv)

The primate visual system has inspired the development of deep artificial neural networks, which have revolutionized the computer vision domain. Yet these networks are much less energy-efficient than their biological counterparts, and they are typically trained with backpropagation, which is extremely data-hungry. To address these limitations, we used a deep convolutional spiking neural network (DCSNN) and a latency-coding scheme. We trained it using a combination of spike-timing-dependent plasticity (STDP) for the lower layers and reward-modulated STDP (R-STDP) for the higher ones. In short, with R-STDP a correct (resp. incorrect) decision leads to STDP (resp. anti-STDP). This approach led to an accuracy of 97.2% on MNIST, without requiring an external classifier. In addition, we demonstrated that R-STDP extracts features that are diagnostic for the task at hand, and discards the other ones, whereas STDP extracts any feature that repeats. Finally, our approach is biologically plausible, hardware friendly, and energy-efficient.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2018.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 6; 2024: 3; 2025: 6; 2026: 3.

25 papers.

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  1. 2023Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorizationcited by 34
  2. 2021Characterization of Generalizability of Spike Timing Dependent Plasticity trained Spiking Neural Networkscited by 8
  3. 2024Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applicationscited by 7
  4. 2023Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networkscited by 5
  5. 2021A Microarchitecture Implementation Framework for Online Learning with Temporal Neural Networkscited by 4
  6. 2021SpikePropamine: Differentiable Plasticity in Spiking Neural Networkscited by 4
  7. 2024Neuronal Competition Groups with Supervised STDP for Spike-Based Classificationcited by 4
  8. 2021Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networkscited by 3
  9. 2025Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspectivecited by 3
  10. 2022A Faster Approach to Spiking Deep Convolutional Neural Networkscited by 2
  11. 2022Models Developed for Spiking Neural Networkscited by 2
  12. 2023Meta-Learning in Spiking Neural Networks with Reward-Modulated STDPcited by 2
13 more
  1. 2025SAEN-BGS: Energy-Efficient Spiking AutoEncoder Network for Background Subtractioncited by 2
  2. 2021BioLCNet: Reward-modulated Locally Connected Spiking Neural Networkscited by 1
  3. 2023Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networkscited by 1
  4. 2023Spyker: High-performance Library for Spiking Deep Neural Networkscited by 0
  5. 2023Spiking neural networks with Hebbian plasticity for unsupervised representation learningcited by 0
  6. 2024Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classificationcited by 0
  7. 2025On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learningcited by 0
  8. 2025Frozen Backpropagation: Relaxing Weight Symmetry in Deep Spiking Neural Networkscited by 0
  9. 2025Training of Spiking Neural Networks with Expectation-Propagationcited by 0
  10. 2025SMTrack: End-to-End Trained Spiking Neural Networks for Multi-Object Tracking in RGB Videoscited by 0
  11. 2026NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Frameworkcited by 0
  12. 2026Spiking Neural Network Actor-Critic Proximal Policy Optimization Control for Autonomous UAV Navigation Through Constrained Openings in Civil Infrastructure and Buildingscited by 0
  13. 2026Multi-Depth Temporal Fusion for Feedforward, Locally Trained Spiking Neural Networkscited by 0