{"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/a-synapse-threshold-synergistic-learning","title":"A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks","arxiv_id":"2206.06129","date":"2022-06-10","proceeding":null,"authors":["Hongze Sun","Wuque Cai","Baoxin Yang","Yan Cui","Yang Xia","Dezhong Yao","Daqing Guo"],"abstract":"Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike threshold of biological neurons is a critical intrinsic neuronal feature that exhibits rich dynamics on a millisecond timescale and has been proposed as an underlying mechanism that facilitates neural information processing. In this study, we develop a novel synergistic learning approach that involves simultaneously training synaptic weights and spike thresholds in SNNs. SNNs trained with synapse-threshold synergistic learning~(STL-SNNs) achieve significantly superior performance on various static and neuromorphic datasets than SNNs trained with two degenerated single-learning models. During training, the synergistic learning approach optimizes neural thresholds, providing the network with stable signal transmission via appropriate firing rates. Further analysis indicates that STL-SNNs are robust to noisy data and exhibit low energy consumption for deep network structures. Additionally, the performance of STL-SNN can be further improved by introducing a generalized joint decision framework. Overall, our findings indicate that biologically plausible synergies between synaptic and intrinsic non-synaptic mechanisms may provide a promising approach for developing highly efficient SNN learning methods.","url_abs":"https://arxiv.org/abs/2206.06129v3","url_pdf":"https://arxiv.org/pdf/2206.06129v3.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":"a-synapse-threshold-synergistic-learning","repo_url":"https://github.com/sunhongze/STL-SNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"event-data-classification","task_name":"Event data classification"},{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"sound-event-localization-and-detection","task_name":"Sound Event Localization and Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/event-data-classification-on-cifar10-dvs-1","task":"Event data classification","dataset":"CIFAR10-DVS","model":"STL-SNN","rank_in_archive_order":2,"of":7,"metrics":{"Accuracy":"78.50"},"uses_additional_data":false},{"leaderboard":"/sota/gesture-recognition-on-dvs128-gesture","task":"Gesture Recognition","dataset":"DVS128 Gesture","model":"STL-SNN","rank_in_archive_order":7,"of":14,"metrics":{"Accuracy (%)":"97.22"},"uses_additional_data":false},{"leaderboard":"/sota/sound-event-localization-and-detection-on-3","task":"Sound Event Localization and Detection","dataset":"RWCP Sound Scene Database","model":"STL-SNN","rank_in_archive_order":1,"of":1,"metrics":{"accuracy":"98.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}