Skip to main content
SpringerLink
Log in

A Neural Network Model for Retaining Object Information Required in a Categorization Task

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9948))

Included in the following conference series:

  • 2791 Accesses

Abstract

Categorization is our ability to generalize properties of object, and clearly fundamental cognitive capacity. A delayed match-to-categorization task requires working memory of category information, shaped by the interaction between prefrontal cortex (PFC) and inferior temporal (IT) cortex. In the present study, we present the neural mechanism by which working memory is shaped and retained in PFC and how top-down signals from PFC to IT affect the categorization ability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tanaka, K.: Inferotemporal cortex and object vision. Annu. Rev. Neurosci. 19, 109–139 (1996)

    Article  Google Scholar 

  2. Logothetis, N.K., Sheinberg, D.L.: Visual object recognition. Annu. Rev. Neurosci. 19, 577–621 (1996)

    Article  Google Scholar 

  3. Palmeri, T.J., Gauthier, I.: Visual object understanding. Nat. Rev. Neurosci. 5, 291–303 (2004)

    Article  Google Scholar 

  4. Sigala, N., Logothetis, N.K.: Visual categorization shapes feature selectivity in the primate temporal cortex. Nature 415, 318–320 (2002)

    Article  Google Scholar 

  5. Soga, M., Kashimori, Y.: Functional connections between visual areas in extracting object features critical for a visual categorization task. Vis. Res. 49, 337–347 (2009)

    Article  Google Scholar 

  6. Freedman, D.J., Riesenhuber, M., Poggio, T., Miller, E.K.: A comparison of primate prefrontal and inferior temporal cortices during visual categorization. J. Neurosci. 23, 5235–5246 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Engineering Science, University of Electro-Communications, Chofu, Tokyo, 182-8585, Japan

    Yuki Abe, Kazuhisa Fujita & Yoshiki Kashimori

  2. Tsuyama National College of Technology, 654-1 Numa, Tsuyama, Okayama, 708-8506, Japan

    Kazuhisa Fujita

Authors
  1. Yuki Abe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kazuhisa Fujita
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yoshiki Kashimori
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yoshiki Kashimori .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Abe, Y., Fujita, K., Kashimori, Y. (2016). A Neural Network Model for Retaining Object Information Required in a Categorization Task. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46672-9_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46671-2

  • Online ISBN: 978-3-319-46672-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation