Huang Jing, Ruan Xiaogang, Yu Naigong, Fan Qingwu, Li Jiaming, Cai Jianxian
Institute of Artificial Intelligence and Robotics, Beijing University of Technology, Beijing 100124, China; Pilot College, Beijing University of Technology, Beijing 101101, China.
Institute of Artificial Intelligence and Robotics, Beijing University of Technology, Beijing 100124, China.
Comput Intell Neurosci. 2016;2016:4296356. doi: 10.1155/2016/4296356. Epub 2016 Oct 30.
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.
联想学习,包括经典条件反射和操作性条件反射,被认为是动物和人类最基本的学习类型。围绕经典条件反射或操作性条件反射已经提出了许多模型。然而,用于解释这两种条件反射的统一综合模型的研究要少得多。在此,提出了一种基于神经调节突触可塑性的模型。该模型受生物启发,包括多存储记忆模块和模拟腹侧被盖区多巴胺能神经元以产生奖励信号。突触权重根据奖励信号进行修改,这模拟了联想学习中联想强度的变化。在真实机器人上的实验结果证明了所提模型的适用性和有效性。