Art

J-GLOBAL ID：202002288999311756   Reference number：20A0750306

Consideration on liquid structure contributing to discrimination capability of Liquid State Machine

液体状態マシンの識別能力に寄与する液体構造に関する考察

Author (2)： Sakaguchi Tatsuro (Graduated School of Information Science and Technology, Osaka University) ,  Wakamiya Naoki (Graduated School of Information Science and Technology, Osaka University)
Material： Nonlinear Theory and Its Applications, IEICE (Web)  (Nonlinear Theory Its Appl IEICE (Web))
Volume： 11  Issue： 1  Page： 36-59(J-STAGE)  Publication year： 2020 
JST Material Number： U0219A  ISSN： 2185-4106  Document type： Article
Article type： 原著論文  Country of issue： Japan (JPN)  Language： ENGLISH (EN)

Thesaurus term： *computation model ,  liquid structure ,  neural network(biomodel) ,  neural network(network) ,  network structure
Semi thesaurus term： *液体状態マシン ,  recurrent neural network ,  scale free

Author keywords (3)： Liquid State Machine ,  structural properties ,  discrimination

JST classification (1)： Neurocomputers  (JC06010Q)

Reference (25)：

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• [2] B.J. Grzyb, E. Chinellato, G.M. Wojcik, and W.A. Kaminski, “Facial expression recognition based on Liquid State Machines built of alternative neuron models,” Proceedings of the International Joint Conference on Neural Networks, IEEE, pp. 1011-1017, 2009.
• [3] T. Yamazaki and S. Tanaka, “The cerebellum as a liquid state machine,” Neural Networks, vol. 20, no. 3, pp. 290-297, 2007.
• [4] E. Hourdakis and P. Trahanias, “Improving the classification performance of Liquid State Machines based on the separation property,” Engineering Applications of Neural Networks, no. ii, Springer, Berlin, Heidelberg, pp. 52-62, 2011.
• [5] H. Ju, J.X. Xu, E. Chong, and A.M.J. VanDongen, “Effects of synaptic connectivity on liquid state machine performance,” Neural Networks, vol. 38, pp. 39-51, 2013.

Terms in the title (4)： マシン ,  識別 ,  能力 ,  液体構造