# An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks

arXiv: https://arxiv.org/abs/2203.03379 · HTML: https://mlsanity.com/paper/2203.03379/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 7 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation](https://mlsanity.com/paper/1602.05179/) (2016)
- [STDP-based spiking deep convolutional neural networks for object recognition](https://mlsanity.com/paper/1611.01421/) (2016)
- [STDP as presynaptic activity times rate of change of postsynaptic activity](https://mlsanity.com/paper/1509.05936/) (2015)

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

- [S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks](https://mlsanity.com/paper/2306.15220/) (2023)
- [A survey on learning models of spiking neural membrane systems and spiking neural networks](https://mlsanity.com/paper/2403.18609/) (2024)
- [Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing](https://mlsanity.com/paper/2607.04921/) (2026)

## Abstract (arXiv)

Compared with rate-based artificial neural networks, Spiking Neural Networks (SNN) provide a more biological plausible model for the brain. But how they perform supervised learning remains elusive. Inspired by recent works of Bengio et al., we propose a supervised learning algorithm based on Spike-Timing Dependent Plasticity (STDP) for a hierarchical SNN consisting of Leaky Integrate-and-fire (LIF) neurons. A time window is designed for the presynaptic neuron and only the spikes in this window take part in the STDP updating process. The model is trained on the MNIST dataset. The classification accuracy approach that of a Multilayer Perceptron (MLP) with similar architecture trained by the standard back-propagation algorithm.