Summary

Proceedings of the 2013 International Symposium on Nonlinear Theory and its Applications

2013

Session Number:C1L-D

Session:

Number:382

Investigation of Influences of Neurogenesis in Multi-Layer Perceptron

Yuta Yokoyama,  Chihiro Ikuta,  Yoko Uwate,  Yoshifumi Nishio,  

pp.382-385

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.2.382

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Summary:
Neurogenesis is that new neurons are generated in the human brain. We focus on the characteristic of the neurogenesis with biologically. In the previous study, we have proposed artificial network model which was applied the neurogenesis to Multi-Layer Perceptron (MLP).
In this study, we show the effectiveness of the proposed network with neurogenesis for pattern recognition. And we investigate the parameter dependency for detailed research on the influences of neurogenesis on MLP.

References:

[1] S. Becker, J. M.Wojtowicz, “A Model of Hippocampal Neurogenesis in Memory and Mood Disorders,” Cognitive Sciences, vol. 11, no. 2, pp. 70-76, 2007.

[2] R. A. Chambers, M. N. Potenza, R. E. Hoffman, W. Miranker, “Simulated Apotosis/Neurogenesis Regulates Learning and Memory Capabilities of Adaptive Neural Networks,” Neuropsychopharmacology, pp. 747-758, 2004.

[3] H. Satoi, H. Tomimoto, R. Ohtani, T. Kondo, M. Watanabe, N. Oka, I. Akiguchi, S. Furuta, Y. Hirabayashi and T. Okazaki, “Astroglial Expression of Ceramide in Alzheimer's Disease Brains: A Role During Neuronal Apoptosis,” Neuroscience, vol. 130, pp. 657-666, 2005.

[4] Y. Yokoyama, T. Shima, C. Ikuta, Y. Uwate and Y. Nishio, “Improvement of Learning Performance of Neural Network Using Neurogenesis,” Proceedings of RISP International Workshop on Nonlinear Circuits and Signal Processing (NCSP'12), pp. 365-368, Mar. 2012.

[5] Y. Yokoyama, C. Ikuta, Y. Uwate and Y. Nishio, “Performance of Multi-Layer Perceptron with Neurogenesis,” Proceedings of International Symposium on Nonlinear Theory and its Applications (NOLTA'12), pp.715 718, Oct. 2012.

[6] D.E. Rumelhart, G.E. Hinton and R.J.Williams, “Learning Representations by Back Propagation Error,” Nature, vol. 323-9, pp. 533-536, 1986.

[7] D.E. Rumelhart, G.E. Hinton and R.J. Williams, “Learning Internal Representations by Error Propagation,” Parallel Distributed Processing, vol. 1, pp. 318-362, 1986.

[8] D.E. Rumelhart, J.L. McClelland, and the PDP Research Group, “Parallel distributed processing,” MIT Press, 1986.

[9] M Hasegawa, T Ikeguchi, T Matozaki and K Aihara, “An analysis on additive effects of nonlinear dynamics for combinatorial optimization,” IEICE Trans. Fundamentals, vol. E80-A, no. 1, pp. 206-213, 1997.