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Graceful Degradation Under Noise on Brain Inspired Robot Controllers

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9947))

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Abstract

How can we build robot controllers that are able to work under harsh conditions, but without experiencing catastrophic failures? As seen on the recent Fukushima’s nuclear disaster, standard robots break down when exposed to high radiation environments. Here we present the results from two arrangements of Spiking Neural Networks, based on the Liquid State Machine (LSM) framework, that were able to gracefully degrade under the effects of a noisy current injected directly into each simulated neuron. These noisy currents could be seen, in a simplified way, as the consequences of exposition to non-destructive radiation. The results show that not only can the systems withstand noise, but one of the configurations, the Modular Parallel LSM, actually improved its results, in a certain range, when the noise levels were increased. Also, the robot controllers implemented in this work are suitable to run on a modern, power efficient neuromorphic hardware such as SpiNNaker.

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Notes

  1. 1.

    Source code available at github.com/ricardodeazambuja/IJCNN2016.

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Acknowledgment

This work was in part supported by the CAPES Foundation, Ministry of Education of Brazil (scholarship BEX 1084/13-5), CNPq Brazil (scholarship 232590/2014-1) and UK EPSRC project BABEL (EP/J004561/1 and EP/J00457X/1).

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Authors and Affiliations

  1. School of Computing, Electronics and Mathematics, Plymouth University, Plymouth, UK

    Ricardo de Azambuja, Frederico B. Klein, Martin F. Stoelen, Samantha V. Adams & Angelo Cangelosi

  2. CAPES Foundation, Ministry of Education of Brazil, Brasilia, 70040-020, Brazil

    Ricardo de Azambuja

Authors
  1. Ricardo de Azambuja
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  2. Frederico B. Klein
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  3. Martin F. Stoelen
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  4. Samantha V. Adams
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  5. Angelo Cangelosi
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Corresponding author

Correspondence to Ricardo de Azambuja .

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

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de Azambuja, R., Klein, F.B., Stoelen, M.F., Adams, S.V., Cangelosi, A. (2016). Graceful Degradation Under Noise on Brain Inspired Robot Controllers. 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 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_21

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  • DOI: https://doi.org/10.1007/978-3-319-46687-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46686-6

  • Online ISBN: 978-3-319-46687-3

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