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首页> 外文期刊>The European Journal of Neuroscience >Beyond simple reinforcement learning: The computational neurobiology of reward-learning and valuation
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Beyond simple reinforcement learning: The computational neurobiology of reward-learning and valuation

机译:超越简单的强化学习:奖励学习和评估的计算神经生物学

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摘要

Neural computational accounts of reward-learning have been dominated by the hypothesis that dopamine neurons behave like a reward-prediction error and thus facilitate reinforcement learning in striatal target neurons. While this framework is consistent with a lot of behavioral and neural evidence, this theory fails to account for a number of behavioral and neurobiological observations. In this special issue of EJN we feature a combination of theoretical and experimental papers highlighting some of the explanatory challenges faced by simple reinforcement-learning models and describing some of the ways in which the framework is being extended in order to address these challenges.
机译:奖励学习的神经计算方法已被以下假设所支配:多巴胺神经元的行为类似于奖励预测错误,因此有助于纹状体目标神经元的强化学习。尽管此框架与许多行为和神经证据相一致,但该理论未能说明许多行为和神经生物学的观察结果。在EJN的这一期特刊中,我们结合了理论和实验论文,重点介绍了简单的强化学习模型所面临的一些解释性挑战,并描述了扩展框架以应对这些挑战的一些方式。

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