Paper ID: 2410.16524 • Published Oct 21, 2024
Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image Recognition
Wei Xie
TL;DR
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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.