{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gradient-free-supervised-learning-using-spike","title":"Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image Recognition","arxiv_id":"2410.16524","date":"2024-10-21","proceeding":null,"authors":["Wei Xie"],"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.","url_abs":"https://arxiv.org/abs/2410.16524v2","url_pdf":"https://arxiv.org/pdf/2410.16524v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gradient-free-supervised-learning-using-spike","repo_url":"https://github.com/wxie2013/gdfree-supervised-snn-mnist","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"one-shot-learning","task_name":"One-Shot Learning"}],"methods":[{"method_slug":"snn","method_name":"SNN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}