Coincidence Detection Using Spiking Neurons with Application to Face Recognition
We elucidate the practical implementation of Spiking Neural Network (SNN) as local ensembles of classifiers. Synaptic time constant τs 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)...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2015/534198 |
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| author | Fadhlan Kamaruzaman Amir Akramin Shafie Yasir M. Mustafah |
| author_facet | Fadhlan Kamaruzaman Amir Akramin Shafie Yasir M. Mustafah |
| author_sort | Fadhlan Kamaruzaman |
| collection | DOAJ |
| description | We elucidate the practical implementation of Spiking Neural Network (SNN) as local ensembles of classifiers. Synaptic time constant τs 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 τs Prediction which adjusts the precise timing of output spikes towards the desired spike timing through iterative adaptation of τs. 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 k nearest neighbor, ensembles of kNN, 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. |
| format | Article |
| id | doaj-art-56bd95940f564696ba1cead5557642a8 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-56bd95940f564696ba1cead5557642a82025-02-03T01:32:04ZengWileyJournal of Applied Mathematics1110-757X1687-00422015-01-01201510.1155/2015/534198534198Coincidence Detection Using Spiking Neurons with Application to Face RecognitionFadhlan Kamaruzaman0Amir Akramin Shafie1Yasir M. Mustafah2Department of Mechatronics Engineering, International Islamic University Malaysia, P.O. Box 10, 50728 Kuala Lumpur, MalaysiaDepartment of Mechatronics Engineering, International Islamic University Malaysia, P.O. Box 10, 50728 Kuala Lumpur, MalaysiaDepartment of Mechatronics Engineering, International Islamic University Malaysia, P.O. Box 10, 50728 Kuala Lumpur, MalaysiaWe elucidate the practical implementation of Spiking Neural Network (SNN) as local ensembles of classifiers. Synaptic time constant τs 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 τs Prediction which adjusts the precise timing of output spikes towards the desired spike timing through iterative adaptation of τs. 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 k nearest neighbor, ensembles of kNN, 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.http://dx.doi.org/10.1155/2015/534198 |
| spellingShingle | Fadhlan Kamaruzaman Amir Akramin Shafie Yasir M. Mustafah Coincidence Detection Using Spiking Neurons with Application to Face Recognition Journal of Applied Mathematics |
| title | Coincidence Detection Using Spiking Neurons with Application to Face Recognition |
| title_full | Coincidence Detection Using Spiking Neurons with Application to Face Recognition |
| title_fullStr | Coincidence Detection Using Spiking Neurons with Application to Face Recognition |
| title_full_unstemmed | Coincidence Detection Using Spiking Neurons with Application to Face Recognition |
| title_short | Coincidence Detection Using Spiking Neurons with Application to Face Recognition |
| title_sort | coincidence detection using spiking neurons with application to face recognition |
| url | http://dx.doi.org/10.1155/2015/534198 |
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