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Associative Memory: An Spiking Neural Network Robotic Implementation

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Artificial General Intelligence (AGI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10999))

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Abstract

This article proposes a novel minimalist bio-inspired associative memory (AM) mechanism based on a spiking neural network acting as a controller in simple virtual and physical robots. As such, several main features of a general AM concept were reproduced. Using the strength of temporal coding at the single spike resolution level, this study approaches the AM phenomenon with basic examples in the visual modality. Specifically, the AM include varying time delays in synaptic links and asymmetry in the spike-timing dependent plasticity learning rules to solve visual tasks of pattern-matching, pattern-completion and noise-tolerance for autoassociative and heteroassociative memories. This preliminary work could serve as a step toward future comparative analysis with traditional artificial neural networks.

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Notes

  1. 1.

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Author information

Authors and Affiliations

  1. Ottawa University, 75 Laurier Avenue East, Ottawa, ON, K1N 6N5, Canada

    André Cyr, Matthew Ross & Sylvain Chartier

  2. Cegep du Vieux Montréal, 255, Ontario Est, Montréal, QC, H2X 1X6, Canada

    Frédéric Thériault

Authors
  1. André Cyr
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  2. Frédéric Thériault
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  3. Matthew Ross
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  4. Sylvain Chartier
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Corresponding author

Correspondence to André Cyr .

Editor information

Editors and Affiliations

  1. Adams State University, Alamosa, Colorado, USA

    Matthew Iklé

  2. Odessa Competence Center for Artificial Intelligence (OCCAM), Odessa, Ukraine

    Arthur Franz

  3. Hokkaido University, Sapporo, Japan

    Rafal Rzepka

  4. SingularityNET; OpenCog; Hanson Robotics, Hong Kong, China

    Ben Goertzel

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Cyr, A., Thériault, F., Ross, M., Chartier, S. (2018). Associative Memory: An Spiking Neural Network Robotic Implementation. In: Iklé, M., Franz, A., Rzepka, R., Goertzel, B. (eds) Artificial General Intelligence. AGI 2018. Lecture Notes in Computer Science(), vol 10999. Springer, Cham. https://doi.org/10.1007/978-3-319-97676-1_4

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  • DOI: https://doi.org/10.1007/978-3-319-97676-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97675-4

  • Online ISBN: 978-3-319-97676-1

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