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In this article, we propose an unsupervised bioplausible learning rule for adjusting the synaptic delays in spiking neural networks. We also provide the mathematical proofs to show the convergence of our rule in learning spatiotemporal patterns. Furthermore, to show the effectiveness of our learning rule, we conducted several experiments on random dot kinematogram and a subset of DVS128 Gesture data sets. The experimental results indicate the efficiency of applying our proposed delay learning rule in extracting spatiotemporal features in an STDP-based spiking neural network.<\/jats:p>","DOI":"10.1162\/neco_a_01674","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T22:53:02Z","timestamp":1716418382000},"page":"1332-1352","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":4,"title":["Bioplausible Unsupervised Delay Learning for Extracting Spatiotemporal Features in Spiking Neural Networks"],"prefix":"10.1162","volume":"36","author":[{"given":"Alireza","family":"Nadafian","sequence":"first","affiliation":[{"name":"School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran 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