Department of Psychological Sciences, Purdue University West Lafayette, IN, USA.
Department of Biotechnology, Indian Institute of Technology Madras, India.
Front Comput Neurosci. 2013 Dec 6;7:174. doi: 10.3389/fncom.2013.00174.
Many computational models of the basal ganglia (BG) have been proposed over the past twenty-five years. While computational neuroscience models have focused on closely matching the neurobiology of the BG, computational cognitive neuroscience (CCN) models have focused on how the BG can be used to implement cognitive and motor functions. This review article focuses on CCN models of the BG and how they use the neuroanatomy of the BG to account for cognitive and motor functions such as categorization, instrumental conditioning, probabilistic learning, working memory, sequence learning, automaticity, reaching, handwriting, and eye saccades. A total of 19 BG models accounting for one or more of these functions are reviewed and compared. The review concludes with a discussion of the limitations of existing CCN models of the BG and prescriptions for future modeling, including the need for computational models of the BG that can simultaneously account for cognitive and motor functions, and the need for a more complete specification of the role of the BG in behavioral functions.
过去 25 年来,已经提出了许多基底神经节(BG)的计算模型。虽然计算神经科学模型侧重于密切匹配 BG 的神经生物学,但计算认知神经科学(CCN)模型侧重于 BG 如何用于实现认知和运动功能。本文综述重点介绍了用于 BG 的 CCN 模型,以及它们如何利用 BG 的神经解剖结构来解释认知和运动功能,例如分类、工具性条件作用、概率学习、工作记忆、序列学习、自动化、伸手、手写和眼球扫视。综述共回顾和比较了 19 个用于一个或多个这些功能的 BG 模型。综述最后讨论了现有 BG 的 CCN 模型的局限性和未来建模的建议,包括需要能够同时解释认知和运动功能的 BG 的计算模型,以及需要更完整地说明 BG 在行为功能中的作用。