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

TL;DR

This paper presents a new supervised learning method for spiking neural networks that uses spike-timing-dependent plasticity instead of backpropagation, achieving high accuracy on image recognition with minimal training stimuli.

Contribution

It introduces a novel supervised learning framework using STDP for spiking neural networks, eliminating the need for backpropagation in image recognition tasks.

Findings

Achieves 40% accuracy with 10 stimuli per class

Reaches 87% accuracy with larger training sets

Attains 89% accuracy with only 10 hidden neurons

Abstract

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.