A Cognitive Model Based on Neuromodulated Plasticity

• Main Authors: Jing Huang, Xiaogang Ruan, Naigong Yu, Qingwu Fan, Jiaming Li, Jianxian Cai
• Format: Article
• Language: English
• Published: Hindawi Limited 2016-01-01
• Series: Computational Intelligence and Neuroscience
• Online Access: http://dx.doi.org/10.1155/2016/4296356

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.