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基底神经节的多重控制:纹状体亚区的计算作用。

Multiplicity of control in the basal ganglia: computational roles of striatal subregions.

机构信息

Center for Neural Science and Psychology Department, New York University, 4 Washington Place, New York, NY 10003, USA.

出版信息

Curr Opin Neurobiol. 2011 Jun;21(3):374-80. doi: 10.1016/j.conb.2011.02.009. Epub 2011 Mar 21.

Abstract

The basal ganglia, in particular the striatum, are central to theories of behavioral control, and often identified as a seat of action selection. Reinforcement learning (RL) models--which have driven much recent experimental work on this region--cast striatum as a dynamic controller, integrating sensory and motivational information to construct efficient and enriching behavioral policies. Befitting this informationally central role, the BG sit at the nexus of multiple anatomical 'loops' of synaptic projections, connecting a wide range of cortical and subcortical structures. Numerous pioneering anatomical studies conducted over the past several decades have meticulously catalogued these loops, and labeled them according to the inferred functions of the connected regions. The specific cotermina of the projections are highly localized to several different subregions of the striatum, leading to the suggestion that these subregions perform complementary but distinct functions. However, until recently, the dominant computational framework outlined only a bipartite, dorsal/ventral, division of striatum. We review recent computational and experimental advances that argue for a more finely fractionated delineation. In particular, experimental data provide extensive insight into unique functions subserved by the dorsomedial striatum (DMS). These functions appear to correspond well with theories of a 'model-based' RL subunit, and may also shed light on the suborganization of ventral striatum. Finally, we discuss the limitations of these ideas and how they point the way toward future refinements of neurocomputational theories of striatal function, bringing them into contact with other areas of computational theory and other regions of the brain.

摘要

基底神经节,特别是纹状体,是行为控制理论的核心,通常被认为是动作选择的所在地。强化学习(RL)模型——这推动了该区域最近的大量实验工作——将纹状体视为一个动态控制器,整合感觉和动机信息,构建高效和丰富的行为策略。符合这种信息中心的作用,BG 位于多个解剖学“环路”的突触投射的交点处,连接着广泛的皮质和皮质下结构。过去几十年进行的许多开创性解剖学研究详细记录了这些环路,并根据连接区域的推断功能对其进行了标记。这些投射的特定终点高度局限于纹状体的几个不同亚区,这表明这些亚区执行互补但不同的功能。然而,直到最近,主导的计算框架仅概述了纹状体的二分,背/腹部分。我们回顾了最近的计算和实验进展,这些进展支持更精细的细分。特别是,实验数据提供了对背内侧纹状体(DMS)所服务的独特功能的广泛了解。这些功能似乎与 RL 亚单位的“基于模型”理论非常吻合,并且也可能阐明了腹侧纹状体的亚组织。最后,我们讨论了这些想法的局限性,以及它们如何为纹状体功能的神经计算理论的未来改进指明方向,使它们与计算理论的其他领域和大脑的其他区域联系起来。

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