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To address this issue, the practical solution is to retrain the network to maintain performance, leveraging its rapid, incremental online learning capacity. In this paper, we develop a bio-inspired spiking neural network (SNN) to recognize nine types of flammable and toxic gases, which supports few-shot class-incremental learning, and can be retrained quickly with a new gas at a low accuracy cost. Compared with gas recognition approaches such as support vector machine (SVM), k-nearest neighbor (KNN), principal component analysis (PCA) +SVM, PCA+KNN, and artificial neural network (ANN), our network achieves the highest accuracy of 98.75% in five-fold cross-validation for identifying nine types of gases, each with five different concentrations. In particular, the proposed network has a 5.09% higher accuracy than that of other gas recognition algorithms, which validates its robustness and effectiveness for real-life fire scenarios.<\/jats:p>","DOI":"10.3390\/s23052433","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T02:01:25Z","timestamp":1677117685000},"page":"2433","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Bio-Inspired Spiking Neural Network with Few-Shot Class-Incremental Learning for Gas Recognition"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0452-3998","authenticated-orcid":false,"given":"Dexuan","family":"Huo","sequence":"first","affiliation":[{"name":"School of Integrated Circuits, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jilin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pingping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Suzhou Huiwen Nanotechnology Co., Ltd., Suzhou 215004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shumin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Suzhou Huiwen Nanotechnology Co., Ltd., Suzhou 215004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Yang","sequence":"additional","affiliation":[{"name":"Suzhou Huiwen Nanotechnology Co., Ltd., Suzhou 215004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiachuang","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengwei","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuhui","family":"Sun","sequence":"additional","affiliation":[{"name":"Suzhou Huiwen Nanotechnology Co., Ltd., Suzhou 215004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wen, Z., Ye, W., Zhao, X., and Pan, X. 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