Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image Recognition
This provides a robust and efficient alternative to backpropagation for spiking neural networks, though it is incremental as it applies a known mechanism (STDP) in a supervised context.
The paper tackles supervised learning for spiking neural networks by replacing backpropagation with spike-timing-dependent plasticity (STDP) for image recognition, achieving up to 89% accuracy on MNIST with minimal training and hidden neurons.
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.