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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 4296356, 15 pages
Research Article

A Cognitive Model Based on Neuromodulated Plasticity

1Institute of Artificial Intelligence and Robotics, Beijing University of Technology, Beijing 100124, China
2Pilot College, Beijing University of Technology, Beijing 101101, China

Received 6 March 2016; Revised 17 July 2016; Accepted 22 September 2016

Academic Editor: Leonardo Franco

Copyright © 2016 Jing Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Associative learning, including classical conditioning and operant conditioning, is regarded as the most fundamental type of learning for animals and human beings. Many models have been proposed surrounding classical conditioning or operant conditioning. However, a unified and integrated model to explain the two types of conditioning is much less studied. Here, a model based on neuromodulated synaptic plasticity is presented. The model is bioinspired including multistored memory module and simulated VTA dopaminergic neurons to produce reward signal. The synaptic weights are modified according to the reward signal, which simulates the change of associative strengths in associative learning. The experiment results in real robots prove the suitability and validity of the proposed model.