A Neurocomputational Model for Cocaine Addiction
A Neurocomputational Model for Cocaine Addiction
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DOI：
10.1162/neco.2009.10-08-882
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发表时间：
2009-10-01
影响因子：
2.9
通讯作者：
Mokri, Azarakhsh
中科院分区：
文献类型：
--
作者：
Dezfouli, Amir;Piray, Payam;Mokri, Azarakhsh
基于可卡因成瘾的多巴胺假说和长期吸毒后大脑奖赏系统敏感度降低的假说，我们提出了可卡因成瘾的计算模型。利用平均奖赏时差强化学习，将长期吸毒后基础奖赏阈值的升高纳入Reish提出的药物成瘾模型。我们的模型与惩罚下寻求毒品的动物模型是一致的。在非毒品奖励的情况下，该模型解释了长期接触毒品后增加的冲动。此外，我们的模型还预测了可卡因的阻断效应的存在。
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.