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

arXiv: https://arxiv.org/abs/1804.00227 · HTML: https://mlsanity.com/paper/1804.00227/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2018.

## Built on by (most cited first, 27 of 27)

- [Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization](https://mlsanity.com/paper/2305.02099/) (2023)
- [Characterization of Generalizability of Spike Timing Dependent Plasticity trained Spiking Neural Networks](https://mlsanity.com/paper/2105.14677/) (2021)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks](https://mlsanity.com/paper/2308.02194/) (2023)
- [A Microarchitecture Implementation Framework for Online Learning with Temporal Neural Networks](https://mlsanity.com/paper/2105.13262/) (2021)
- [SpikePropamine: Differentiable Plasticity in Spiking Neural Networks](https://mlsanity.com/paper/2106.02681/) (2021)
- [Neuronal Competition Groups with Supervised STDP for Spike-Based Classification](https://mlsanity.com/paper/2410.17066/) (2024)
- [Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks](https://mlsanity.com/paper/2102.09200/) (2021)
- [Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective](https://mlsanity.com/paper/2504.05341/) (2025)
- [A Faster Approach to Spiking Deep Convolutional Neural Networks](https://mlsanity.com/paper/2210.17442/) (2022)
- [Models Developed for Spiking Neural Networks](https://mlsanity.com/paper/2212.04377/) (2022)
- [Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP](https://mlsanity.com/paper/2306.04410/) (2023)
- [SAEN-BGS: Energy-Efficient Spiking AutoEncoder Network for Background Subtraction](https://mlsanity.com/paper/2505.07336/) (2025)
- [BioLCNet: Reward-modulated Locally Connected Spiking Neural Networks](https://mlsanity.com/paper/2109.05539/) (2021)
- [Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks](https://mlsanity.com/paper/2304.01015/) (2023)
- [Spyker: High-performance Library for Spiking Deep Neural Networks](https://mlsanity.com/paper/2301.13659/) (2023)
- [Spiking neural networks with Hebbian plasticity for unsupervised representation learning](https://mlsanity.com/paper/2305.03866/) (2023)
- [Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification](https://mlsanity.com/paper/2407.08746/) (2024)
- [On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learning](https://mlsanity.com/paper/2504.08897/) (2025)
- [Frozen Backpropagation: Relaxing Weight Symmetry in Deep Spiking Neural Networks](https://mlsanity.com/paper/2505.13741/) (2025)
- [Training of Spiking Neural Networks with Expectation-Propagation](https://mlsanity.com/paper/2506.23757/) (2025)
- [Fully Spiking Actor-Critic Neural Network for Robotic Manipulation](https://mlsanity.com/paper/2508.12038/) (2025)
- [SMTrack: End-to-End Trained Spiking Neural Networks for Multi-Object Tracking in RGB Videos](https://mlsanity.com/paper/2508.14607/) (2025)
- [NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework](https://mlsanity.com/paper/2605.15058/) (2026)
- [Spiking Neural Network Actor-Critic Proximal Policy Optimization Control for Autonomous UAV Navigation Through Constrained Openings in Civil Infrastructure and Buildings](https://mlsanity.com/paper/2609.23643/) (2026)
- [Unsupervised spiking feature learning for event-based pedestrian crossing detection: approaching supervised accuracy without labelled training data](https://mlsanity.com/paper/2609.31671/) (2026)
- [Multi-Depth Temporal Fusion for Feedforward, Locally Trained Spiking Neural Networks](https://mlsanity.com/paper/2609.37047/) (2026)

## 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.