Data processing: artificial intelligence – Neural network
Reexamination Certificate
2005-10-21
2010-02-16
Vincent, David R (Department: 2129)
Data processing: artificial intelligence
Neural network
C706S045000
Reexamination Certificate
active
07664714
ABSTRACT:
A neural network element, outputting an output signal in response to a plurality of input signals, comprises a history memory for accumulating and storing the plurality of input signals in a temporal order as history values. It also includes an output module for outputting the output signal when an internal state exceeds a predetermined threshold value, the internal state being based on a sum of the product of a plurality of input signals and corresponding coupling coefficients. The history values depend on change of the internal state. The neural network element is configured to subtract a predetermined value from the internal state immediately after the output module fires and performs learning for reinforcing or attenuating the coupling coefficient according to the history values after the output module fires.
REFERENCES:
patent: 5113482 (1992-05-01), Lynne
patent: 5222195 (1993-06-01), Alkon et al.
patent: 5704016 (1997-12-01), Shigematsu et al.
patent: 6169981 (2001-01-01), Werbos
patent: 6532454 (2003-03-01), Werbos
patent: 2002/0198854 (2002-12-01), Berenji et al.
patent: 2003/0074338 (2003-04-01), Young et al.
patent: 2003/0149676 (2003-08-01), Kasabov
patent: 2004/0054636 (2004-03-01), Tango-Lowy
patent: 1016981 (2000-07-01), None
patent: 07-182433 (1995-07-01), None
patent: 2000-035956 (2000-02-01), None
Gen Matsumoto and Hiroshi Tsujino, Design of a brain computer using the novel principles of output-driven operation and memory-based architecture: in Cognition and Emotion in the Brain (T. Ono, G. Matsumoto, R. Llinas, A. Berthoz, R. Norgen, H. Nishijo, R. Tamura Eds.), Elsevier, ICS1250, 529-546, Dec. 2003.
Hiroshi Tsujino, Output-driven operation and memory-based architecture principles embedded in a real-world device, Journal of Integrative Neuroscience, 3(2), 133-142, Jun. 2004.
Gen Matsumoto, Hiroshi Tsujino (“Design of a brain computer using the novel principles of output-driven operation and memory-based architecture” International Congress Series 2003.
Barto, A.G. et al., “Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems,” IEEE Transactions on Systems, Man and Cybernetics, Sep./Oct. 1983, pp. 834-846, vol. SMC-13, No. 5.
Matsumoto, G. et al., “Design of a Brain Computer Using the Novel Principles of Output-Driven Operation and Memory-Based Architecture” in Cognition and Emotion in the Brain: Selected Topics of the International Symposium on Limbic and Association Cortical Systems (T. Ono, G. Matsumoto, R. Llinas, A. Berthoz, R. Norgren, H. Nishijo, R. Tamura Eds.), held in Toyama, Japan, Oct. 7-12, 2002, International Congress Series No. ICS 1250, 2003, pp. 529-546, Elsevier.
Tsujino, H., “Output-Driven Operation and Memory-Based Architecture Principles Embedded in a Real-World Device,” Journal of Integrative Neuroscience, Jun. 2004, pp. 133-142, vol. 3, No. 2, Imperial College Press.
Tsujino, H., “A System Theory for the Brain-Like Computer,” presented at the Joint 2ndInternational Conference on Soft Computing and Intelligent Systems (SCIS) and 5th International Symposium on Advanced Intelligent Systems (ISIS), Sep. 21-24, 2004, Keio University, Yokohama, Japan.
Tsujino, H., “http://www.brainvision.co.jp/genspage/hrj—tsujino.htm,” 2003, [online] [Retrieved on Sep. 13, 2007] Retrieved from the Internet <URL: http://www.brainvision.co.jp/genspage/hrj—tsujino.htm>.
Machine translation of JP 2000-035956 A (Feb. 2, 2000; Japan Science & Technology Corp.) into English.
Machine translation of JP 07-182433 A (Jul. 21, 1995; Agency of Ind. Science & Technol.) into English.
European Search Report, EP05023033, Jul. 25, 2006.
European Office Action, EP05023033, Feb. 27, 2007.
Communication Pursuant to Article 94(3) EPC (European Patent Office), European Application No. 05023033.3, Jun. 16, 2009, 4 pages.
Matsumoto Gen
Miyakawa Nobuaki
Noyori, legal representative Ryoji
Tsujino Hiroshi
Fenwick & West LLP
Honda Motor Co. Ltd.
Riken
Vincent David R
Wong Lut
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