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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Associative Memory: An Spiking Neural Network Robotic Implementation

verfasst von : André Cyr, Frédéric Thériault, Matthew Ross, Sylvain Chartier

Erschienen in: Artificial General Intelligence

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Task Analysis for Teaching Cumulative Learners
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Fußnoten
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Metadaten
Titel
Associative Memory: An Spiking Neural Network Robotic Implementation
verfasst von
André Cyr
Frédéric Thériault
Matthew Ross
Sylvain Chartier
Copyright-Jahr
2018
Buch
Artificial General Intelligence

Print ISBN: 978-3-319-97675-4

Electronic ISBN: 978-3-319-97676-1

Copyright-Jahr: 2018

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

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