Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke

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• 作者：Nikola Kasabov^a ; ^{nkasabov@aut.ac.nz" class="auth_mail}Author Vitae ; Valery Feigin^bAuthor Vitae ; Zeng-Guang Hou^cAuthor Vitae ; Yixiong Chen^cAuthor Vitae ; Linda Liang^aAuthor Vitae ; Rita Krishnamurthi^bAuthor Vitae ; Muhaini Othman^aAuthor Vitae ; Priya Parmar^bAuthor Vitae
• 关键词：Personalised modelling ; Spatio-temporal pattern recognition ; Spiking neural networks ; Evolving connectionist systems ; Stroke occurrence prediction
• 刊名：Neurocomputing
• 出版年：25 June, 2014
• 年：2014
• 卷：134
• 期：Complete
• 页码：269-279
• 全文大小：4104 K

文摘

The paper presents a novel method and system for personalised (individualised) modelling of spatio/spectro-temporal data (SSTD) and prediction of events. A novel evolving spiking neural network reservoir system (eSNNr) is proposed for the purpose. The system consists of spike-time encoding module of continuous value input information into spike trains; a recurrent 3D SNNr; eSNN as an evolving output classifier. Such system is generated for every new individual, using existing data of similar individuals. Subject to proper training and parameter optimisation, the system is capable of accurate spatio-temporal pattern recognition (STPR) and of early prediction of individual events. The method and the system are generic, applicable to various SSTD and classification and prediction problems. As a case study, the method is applied for early prediction of occurrence of stroke on an individual basis. Preliminary experiments demonstrated a significant improvement in accuracy and time of event prediction when using the proposed method when compared with standard machine learning methods, such as MLR, SVM, and MLP. Future development and applications are discussed.