{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:41Z","timestamp":1761176141806,"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>Spiking Neural Networks (SNNs) represent a promising avenue for energy-efficient neuromorphic computing. Despite their potential, SNNs typically underperform compared to Artificial Neural Networks (ANNs) due to their complex spatio-temporal dynamics. To improve learning in these networks, researchers have developed various approaches that account for their unique characteristics\u2014among them, normalization techniques have proven especially important. Recently, online learning algorithms have been explored for SNN training as they update network weights using only temporally local information, avoiding the high memory demands associated with Backpropagation Through Time (BPTT). However, the computational mechanism of online learning, which relies on temporally local information to update weights, hinders the application of integrating effective normalization techniques tailored for SNNs. In this work, we propose a Time-based Statistics Estimation (TSE) method to address limitations in existing normalization strategies for SNNs. We begin by establishing a systematic link between overall statistics and time-step-specific ones, leveraging the decomposability of key statistical measures. This insight allows our proposed TSE method to reliably estimate overall statistics using only recent iterations. Furthermore, the proposed method is compatible with both BPTT and online learning, consistently yielding strong performance across learning paradigms. Experiments on CIFAR-10, CIFAR-100, ImageNet, and DVS-CIFAR10 datasets demonstrate the superior performance of our method on both static and neuromorphic datasets. In particular, our method achieves state-of-the-art performance in online learning for SNN training.<\/jats:p>","DOI":"10.3233\/faia250888","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:45:10Z","timestamp":1761126310000},"source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Learning of Spiking Neural Networks Through Normalization with Time-Based Statistics Estimation"],"prefix":"10.3233","author":[{"given":"Lei","family":"Liu","sequence":"first","affiliation":[{"name":"ZJU-UIUC Institute, Zhejiang University"}]},{"given":"Chengting","family":"Yu","sequence":"additional","affiliation":[{"name":"ZJU-UIUC Institute, Zhejiang University"},{"name":"College of Information Science and Electronic Engineering, Zhejiang University"}]},{"given":"Kainan","family":"Wang","sequence":"additional","affiliation":[{"name":"ZJU-UIUC Institute, Zhejiang University"},{"name":"College of Information Science and Electronic Engineering, Zhejiang University"}]},{"given":"Aili","family":"Wang","sequence":"additional","affiliation":[{"name":"ZJU-UIUC Institute, Zhejiang University"},{"name":"College of Information Science and Electronic Engineering, Zhejiang University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250888","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:45:10Z","timestamp":1761126310000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250888"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250888","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]]}}}