{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:45Z","timestamp":1761176205491,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Training brain-inspired spiking neural networks (SNNs) with multi-timestep backpropagation imposes substantial memory and computational overhead, hindering their scalability and deployment. To address these challenges, we propose a Neuron-Level Back Propagation (NLBP) method, which eliminates the need for temporal unfolding while maintaining competitive performance. We fit the relationship between a neuron\u2019s average input current and its firing rate using an adaptive sigmoid function. Leveraging this mapping, we derive a neuron-level, single-step backpropagation rule that avoids explicit temporal unfolding. This design significantly reduces computational and memory costs when training deep, large-scale SNNs. Furthermore, NLBP unifies the training of integrate-and-fire (IF) and leaky integrate-and-fire (LIF) neuron models within a framework, and supports both soft and hard reset mechanisms, enhancing generality and practicality. Extensive experiments on pattern classification and object detection demonstrate that NLBP achieves competitive accuracy while reducing memory usage by up to 73.07% and training time by 66.04%.<\/jats:p>","DOI":"10.3233\/faia251095","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:51:16Z","timestamp":1761126676000},"source":"Crossref","is-referenced-by-count":0,"title":["NLBP: Efficient Training Spiking Neural Networks with Neuron-Level Back Propagation"],"prefix":"10.3233","author":[{"given":"Zikai","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulong","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longrun","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinqiao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lirong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuo","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251095","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:51:16Z","timestamp":1761126676000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251095"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251095","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}