O'Reilly Randall C, Frank Michael J
Department of Psychology, University of Colorado Boulder, Boulder, CO 80309, USA.
Neural Comput. 2006 Feb;18(2):283-328. doi: 10.1162/089976606775093909.
The prefrontal cortex has long been thought to subserve both working memory (the holding of information online for processing) and executive functions (deciding how to manipulate working memory and perform processing). Although many computational models of working memory have been developed, the mechanistic basis of executive function remains elusive, often amounting to a homunculus. This article presents an attempt to deconstruct this homunculus through powerful learning mechanisms that allow a computational model of the prefrontal cortex to control both itself and other brain areas in a strategic, task-appropriate manner. These learning mechanisms are based on subcortical structures in the midbrain, basal ganglia, and amygdala, which together form an actor-critic architecture. The critic system learns which prefrontal representations are task relevant and trains the actor, which in turn provides a dynamic gating mechanism for controlling working memory updating. Computationally, the learning mechanism is designed to simultaneously solve the temporal and structural credit assignment problems. The model's performance compares favorably with standard backpropagation-based temporal learning mechanisms on the challenging 1-2-AX working memory task and other benchmark working memory tasks.
长期以来，前额叶皮层一直被认为对工作记忆（即在线保存信息以供处理）和执行功能（即决定如何操纵工作记忆并进行处理）均有作用。尽管已经开发出许多工作记忆的计算模型，但执行功能的机制基础仍然难以捉摸，往往归结为一个小人脑模型。本文试图通过强大的学习机制来解构这个小人脑模型，这些机制使前额叶皮层的计算模型能够以一种策略性的、与任务相适应的方式控制自身及其他脑区。这些学习机制基于中脑、基底神经节和杏仁核中的皮层下结构，它们共同构成了一个行动者-评判者架构。评判系统学习哪些前额叶表征与任务相关，并训练行动者，而行动者反过来又提供一种动态门控机制来控制工作记忆的更新。在计算上，学习机制旨在同时解决时间和结构的信用分配问题。在具有挑战性的1-2-AX工作记忆任务和其他基准工作记忆任务上，该模型的表现优于基于标准反向传播的时间学习机制。
