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An Artificial Synaptic Plasticity Mechanism for Classical Conditioning with Neural Networks

Rizzi Raymundo, Caroline and Johnson, Colin Graeme (2014) An Artificial Synaptic Plasticity Mechanism for Classical Conditioning with Neural Networks. In: Zeng, Zhigang and Li, Yangmin and King, Irwin, eds. Advances in Neural Networks – ISNN 2014 11th International Symposium on Neural Networks. Lecture Notes in Computer Science . Springer, Cham, Switzerland, pp. 213-221. ISBN 978-3-319-12435-3. E-ISBN 978-3-319-12436-0. (doi:10.1007/978-3-319-12436-0_24)

Abstract

We present an artificial synaptic plasticity (ASP) mechanism that allows artificial systems to make associations between environmental stimuli and learn new skills at runtime. ASP builds on the classical neural network for simulating associative learning, which is induced through a conditioning-like procedure. Experiments in a simulated mobile robot demonstrate that ASP has successfully generated conditioned responses. The robot has learned during environmental exploration to use sensors added after training, improving its object-avoidance capabilities.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-319-12436-0_24
Uncontrolled keywords: synaptic plasticity; classical conditioning; artificial neural networks
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Colin Johnson
Date Deposited: 04 Nov 2015 09:47 UTC
Last Modified: 14 Oct 2019 10:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/51428 (The current URI for this page, for reference purposes)
Rizzi Raymundo, Caroline: https://orcid.org/0000-0001-9757-2432
Johnson, Colin Graeme: https://orcid.org/0000-0002-9236-6581
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