Dezfouli Amir, Piray Payam, Keramati Mohammad Mahdi, Ekhtiari Hamed, Lucas Caro, Mokri Azarakhsh
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
Neural Comput. 2009 Oct;21(10):2869-93. doi: 10.1162/neco.2009.10-08-882.
Based on the dopamine hypotheses of cocaine addiction and the assumption of decrement of brain reward system sensitivity after long-term drug exposure, we propose a computational model for cocaine addiction. Utilizing average reward temporal difference reinforcement learning, we incorporate the elevation of basal reward threshold after long-term drug exposure into the model of drug addiction proposed by Redish. Our model is consistent with the animal models of drug seeking under punishment. In the case of nondrug reward, the model explains increased impulsivity after long-term drug exposure. Furthermore, the existence of a blocking effect for cocaine is predicted by our model.
基于可卡因成瘾的多巴胺假说以及长期药物暴露后大脑奖赏系统敏感性降低的假设，我们提出了一个可卡因成瘾的计算模型。利用平均奖赏时间差分强化学习，我们将长期药物暴露后基础奖赏阈值的升高纳入了雷迪什提出的药物成瘾模型中。我们的模型与惩罚条件下觅药的动物模型一致。在非药物奖赏的情况下，该模型解释了长期药物暴露后冲动性增加的现象。此外，我们的模型预测了可卡因存在阻断效应。
