# Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image Recognition

arXiv: https://arxiv.org/abs/2410.16524 · HTML: https://mlsanity.com/paper/2410.16524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2024.

## Abstract (arXiv)

This study introduces a novel supervised learning approach for spiking neural networks that does not rely on traditional backpropagation. Instead, it employs spike-timing-dependent plasticity (STDP) within a supervised framework for image recognition tasks. The effectiveness of this method is demonstrated using the MNIST dataset. The model achieves approximately 40% learning accuracy with just 10 training stimuli, where each category is exposed to the model only once during training (one-shot learning). With larger training samples, the accuracy increases up to 87%, maintaining negligible ambiguity. Notably, with only 10 hidden neurons, the model reaches 89% accuracy with around 10% ambiguity. This proposed method offers a robust and efficient alternative to traditional backpropagation-based supervised learning techniques.