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Neural Representation of Object’s Shape at the Electroreceptor Afferents on Electrolocation

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10636))

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Abstract

A weakly electric fish can recognize object’s shape in the complete darkness. The ability to recognize object’s shape is provided by the electrosensory system of the fish. The fish generates an electric field using its electric organs (EOD: electric organ discharge). An object around the fish modulates the self-generated EOD. Electroreceptor afferents on the fish’s body surface convert the EOD amplitude modulation into firings. The fish can extract information about object’s shape from the EOD amplitude modulation using its electrosensory system. In the present study, we calculated the EOD amplitude modulation evoked by objects that were various shapes and firing patterns of the electroreceptor afferents evoked by the EOD amplitude modulation using computer simulation. We found that the EOD amplitude modulation can be represented by firing patterns of the electroreceptor afferents. Furthermore, we demonstrated that the feature of object’s shape appears in the variation of the peak of firing rate with the rotation of the object.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 15K07146.

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Authors and Affiliations

  1. National Institute of Technology, Tsuyama Collage, 654-1 Numa, Tsuyama, Okayama, 708-8506, Japan

    Kazuhisa Fujita

  2. University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, 182-8585, Japan

    Kazuhisa Fujita & Yoshiki Kashimori

Authors
  1. Kazuhisa Fujita
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  2. Yoshiki Kashimori
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Correspondence to Kazuhisa Fujita .

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Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli **e

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Bei**g, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Fujita, K., Kashimori, Y. (2017). Neural Representation of Object’s Shape at the Electroreceptor Afferents on Electrolocation. In: Liu, D., **e, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_89

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  • DOI: https://doi.org/10.1007/978-3-319-70090-8_89

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70089-2

  • Online ISBN: 978-3-319-70090-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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