{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:43:04Z","timestamp":1740134584838,"version":"3.37.3"},"reference-count":31,"publisher":"Wiley","license":[{"start":{"date-parts":[[2015,1,1]],"date-time":"2015-01-01T00:00:00Z","timestamp":1420070400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education, Malaysia","doi-asserted-by":"publisher","award":["MIRGS13-02-001-0001"],"award-info":[{"award-number":["MIRGS13-02-001-0001"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Technology MARA","award":["MIRGS13-02-001-0001"],"award-info":[{"award-number":["MIRGS13-02-001-0001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Applied Mathematics"],"published-print":{"date-parts":[[2015]]},"abstract":"<jats:p>We elucidate the practical implementation of Spiking Neural Network (SNN) as local ensembles of classifiers. Synaptic time constant<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:msub><mml:mrow><mml:mi>\u03c4<\/mml:mi><\/mml:mrow><mml:mrow><mml:mi>s<\/mml:mi><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>is used as learning parameter in representing the variations learned from a set of training data at classifier level. This classifier uses coincidence detection (CD) strategy trained in supervised manner using a novel supervised learning method called<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:msub><mml:mrow><mml:mi>\u03c4<\/mml:mi><\/mml:mrow><mml:mrow><mml:mi>s<\/mml:mi><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>Prediction which adjusts the precise timing of output spikes towards the desired spike timing through iterative adaptation of<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mrow><mml:msub><mml:mrow><mml:mi>\u03c4<\/mml:mi><\/mml:mrow><mml:mrow><mml:mi>s<\/mml:mi><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>. This paper also discusses the approximation of spike timing in Spike Response Model (SRM) for the purpose of coincidence detection. This process significantly speeds up the whole process of learning and classification. Performance evaluations with face datasets such as AR, FERET, JAFFE, and CK+ datasets show that the proposed method delivers better face classification performance than the network trained with Supervised Synaptic-Time Dependent Plasticity (STDP). We also found that the proposed method delivers better classification accuracy than<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>nearest neighbor, ensembles of<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>NN, and Support Vector Machines. Evaluation on several types of spike codings also reveals that latency coding delivers the best result for face classification as well as for classification of other multivariate datasets.<\/jats:p>","DOI":"10.1155\/2015\/534198","type":"journal-article","created":{"date-parts":[[2015,10,4]],"date-time":"2015-10-04T17:05:36Z","timestamp":1443978336000},"page":"1-20","source":"Crossref","is-referenced-by-count":3,"title":["Coincidence Detection Using Spiking Neurons with Application to Face Recognition"],"prefix":"10.1155","volume":"2015","author":[{"given":"Fadhlan","family":"Kamaruzaman","sequence":"first","affiliation":[{"name":"Department of Mechatronics Engineering, International Islamic University Malaysia, P.O. Box 10, 50728 Kuala Lumpur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir Akramin","family":"Shafie","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, International Islamic University Malaysia, P.O. Box 10, 50728 Kuala Lumpur, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasir M.","family":"Mustafah","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, International Islamic University Malaysia, P.O. 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