Neurocomputational models of basal ganglia function in learning, memory and choice.
Neurocomputational models of basal ganglia function in learning, memory and choice.
复制标题
基底神经节的神经计算模型在学习，记忆和选择中起作用。
DOI：
10.1016/j.bbr.2008.09.029
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发表时间：
2009-04-12
影响因子：
2.7
通讯作者：
Frank, Michael J.
中科院分区：
文献类型：
--
作者：
Cohen, Michael X.;Frank, Michael J.
基底神经节（BG）对多种运动、认知和情绪功能的协调至关重要，并在从帕金森病到精神分裂症等多种病理状态下变得功能失调。在这里，我们回顾了在BG和相关电路的神经计算框架内开发的原理，这些原理提供了对它们在行为中的功能角色的见解。我们重点研究了两类模型：一类是包含生物现实主义方面并受功能原则约束的模型，另一类是关注BG更高水平计算目标的更抽象的数学模型。虽然前者可以说更“现实”，但后者在能够用一组相对简单的方程描述系统如何工作的功能原理方面具有互补优势，但不太适合对特定核和神经生理过程的作用做出具体假设。我们回顾了这些模型的基本结构和假设，它们与我们对BG的神经生物学和认知功能的理解的相关性，并提供了现有模型中未明确纳入的生物学细节的潜在作用的更新。从转基因小鼠到多巴胺能操纵、脑深部刺激和人类遗传学的经验研究在很大程度上支持了模型预测，并为进一步完善提供了基础。最后，我们讨论了未来可能的方向和整合不同类型模型的可能方法。
The basal ganglia (BG) are critical for the coordination of several motor, cognitive, and emotional functions and become dysfunctional in several pathological states ranging from Parkinson's disease to Schizophrenia. Here we review principles developed within a neurocomputational framework of BG and related circuitry which provide insights into their functional roles in behavior. We focus on two classes of models: those that incorporate aspects of biological realism and constrained by functional principles, and more abstract mathematical models focusing on the higher level computational goals of the BG. While the former are arguably more “realistic”, the latter have a complementary advantage in being able to describe functional principles of how the system works in a relatively simple set of equations, but are less suited to making specific hypotheses about the roles of specific nuclei and neurophysiological processes. We review the basic architecture and assumptions of these models, their relevance to our understanding of the neurobiological and cognitive functions of the BG, and provide an update on the potential roles of biological details not explicitly incorporated in existing models. Empirical studies ranging from those in transgenic mice to dopaminergic manipulation, deep brain stimulation, and genetics in humans largely support model predictions and provide the basis for further refinement. Finally, we discuss possible future directions and possible ways to integrate different types of models.
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影响因子：
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通讯作者：
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