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Recent Advances in the Neocognitron

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Neural Information Processing (ICONIP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4984))

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Abstract

The neocognitron is a hierarchical multilayered neural network capable of robust visual pattern recognition. This paper discusses recent advances in the neocognitron, showing several types of neocognitron, to which various improvements and modifications have been made.

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References

  1. Fukushima, K.: Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Networks 1, 119–130 (1988)

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  2. Fukushima, K.: Neocognitron for handwritten digit recognition. Neurocomputing 51, 161–180 (2003); A computer program of this neocognitron in C language is available from Visiome Platform: http://platform.visiome.neuroinf.jp/

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  3. Fukushima, K.: Analysis of the process of visual pattern recognition by the neocognitron. Neural Networks 2, 413–420 (1989)

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  4. Fukushima, K.: Interpolating vectors for robust pattern recognition. Neural Networks 20, 904–916 (2007)

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  5. Fukushima, K.: Neocognitron capable of incremental learning. Neural Networks 17, 37–46 (2004)

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  6. Fukushima, K.: Neural network model for selective attention in visual pattern recognition and associative recall. Applied Optics 26, 4985–4992 (1987)

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  7. Fukushima, K.: Restoring partly occluded patterns: a neural network model. Neural Networks 18, 33–43 (2005)

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  8. Fukushima, K.: Recognition of partly occluded patterns: a neural network model. Biological Cybernetics 84, 251–259 (2001)

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Author information

Authors and Affiliations

  1. Kansai University, Takatsuki, Osaka, 569–1095, Japan

    Kunihiko Fukushima

Authors
  1. Kunihiko Fukushima
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Editor information

Masumi Ishikawa Kenji Doya Hiroyuki Miyamoto Takeshi Yamakawa

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© 2008 Springer-Verlag Berlin Heidelberg

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Fukushima, K. (2008). Recent Advances in the Neocognitron. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_107

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  • DOI: https://doi.org/10.1007/978-3-540-69158-7_107

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69154-9

  • Online ISBN: 978-3-540-69158-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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