Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Implementation of Artificial Hippocampus Algorithm Using Weight Modulator

가중치 모듈레이터를 이용한 인공 해마 알고리즘 구현

  • Published : 2007.05.01
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Abstract

In this paper, we propose the development of Artificial Hippocampus Algorithm(AHA) which remodels a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 4 steps system (EC, DG CA3, and CA1) and improve speed of teaming by addition of modulator to long-term memory teaming. In hippocampus system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labeled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CA1 region, convergence of connection weight which is used long-term memory is learned fast a by neural network which is applied modulator. To measure performance of Artificial Hippocampus Algorithm, PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) are applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by AHA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

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References

  1. P.Dayan and L. F. Abbott,'Theoretical neuroscience,' MIT press, 2001
  2. 민병경, '민병경의 재미있는 뇌 과학 이야기,' http://mind 21. net/scienceall-7.htm
  3. 이케가야 유지, 이토이 시게사토, '해마,' 은행나무, 2003
  4. I. B. Levitan and L. F. Abbot, 'Theoretical neuroscience,' MIT press, 200I
  5. 오창석, 뉴로 컴퓨터 개론, 내하출판사, pp. 234-266, 2000
  6. G E. Hinton and Sejnowski,T.J.Edited,'Unsupervised learning: foundations of neural computation,' MIT Press, Cambridge, Massachusetts, 1999
  7. 김우성, 박현진, 양현승, '비교사 학습을 이용한 얼굴 인식 모델의 실험 및 분석,' 한국과학재단, Tech Report 2002-76, 2002
  8. http://www.isip.msstate.edu/publications/reports/isipjntemal/19 98/linear_discrim_analysis/lda_theory.pdf
  9. M.-H. Yang, 'Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods,' Automatic Face and Gesture Recognition, 2002. Proceedings. Fourth IEEE International Conference on, pp. 208-213,2002
  10. A. Martinez and A. Kak: 'PCA versus LDA,' IEEE Trans. on PAMI, 23(2):228-233, 2001 https://doi.org/10.1109/34.908974
  11. D. W. Zhao, R. Chellappa, 'Discriminant analysis of principal components for face recognition,' Automatic Face and Gesture Recognition, 1998
  12. D. J. R. Beveridge and K. She, 'Fall 2001 update to CSU PCAVersus PCA+ LDA comparison,' http://www.cs.colostate./ edu/evalfacerec/papers.html, 2001
  13. J. Yang and J.-Y Yang, 'Why can LDA be performed in PCA transformedspace?,' Pattern Recognition 36, pp. 563-566, 2003 https://doi.org/10.1016/S0031-3203(02)00048-1
  14. McClelland, J. McNaughton, B, O'Reilly. R. 'Why there are complementary leaming systems in the hippocampus and neocortex,'CMU Tech Report PDP.CNS. 94.1,March 1994
  15. Homepage of the AT&T Laboratories at Cambridge: http:// www.uk.research.att.com/facedatabase.html/