Skip to main content

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) (KAR id:51428)

PDF Publisher pdf
Language: English
Download (268kB) Preview
[thumbnail of chp%3A10.1007%2F978-3-319-12436-0_24.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL


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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Colin Johnson
Date Deposited: 04 Nov 2015 09:47 UTC
Last Modified: 16 Feb 2021 13:29 UTC
Resource URI: (The current URI for this page, for reference purposes)
Rizzi Raymundo, Caroline:
Johnson, Colin Graeme:
  • Depositors only (login required):


Downloads per month over past year