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Despite the presence of multiple coexisting learning strategies in the brain, current SNN training methods typically rely on a single form of synaptic plasticity, which limits their adaptability and representational capability. In this paper, we propose a biology-motivated computational framework that incorporates multiple synergistic plasticity mechanisms for more effective SNN training. The framework is inspired by the coexistence of heterogeneous regulatory and plasticity-related processes in biological neural systems, rather than directly corresponding to specific biological learning rules. Our method enables diverse learning algorithms to cooperatively modulate the accumulation of information, while allowing each mechanism to preserve its own relatively independent update dynamics. We evaluated our approach on diverse datasets to demonstrate that our framework significantly improves performance and robustness compared to conventional learning mechanism models. This work provides a general and extensible foundation for developing more powerful SNNs guided by multi-strategy brain-inspired learning.<\/jats:p>","DOI":"10.3389\/fnins.2026.1939650","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T05:59:13Z","timestamp":1788933553000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-plasticity synergy with adaptive mechanism assignment for training spiking neural networks"],"prefix":"10.3389","volume":"20","author":[{"given":"Zhibin","family":"Li","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiteng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhe","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxiang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susanna","family":"Gordleeva","sequence":"additional","affiliation":[{"name":"Institute of Neurosciences, National Research Lobachevsky State University of Nizhny Novgorod","place":["Nizhny Novgorod, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University","place":["Tianjin, China"]},{"name":"Institute for Embodied Intelligence, Tianjin University","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1109\/72.125866","article-title":"Global optimization of a neural network-hidden Markov model hybrid","volume":"3","author":"Bengio","year":"1992","journal-title":"IEEE transactions on Neural Netw"},{"key":"B2","doi-asserted-by":"publisher","first-page":"10464","DOI":"10.1523\/JNEUROSCI.18-24-10464.1998","article-title":"Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and post-synaptic cell type","volume":"18","author":"Bi","year":"1998","journal-title":"J. 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