The basal ganglia (BG) have been hypothesized to perform reinforcement learning by use of reinforcement signals provided by dopamine neurons. It is well known that there exist multiple BG-thalamocortical loops, but their functions are poorly understood. Here, the authors propose a computational model of how different BG loops are employed in visuomotor sequence learning using different representations of sequence. The central idea of the model is that a visuomotor sequence is easier to learn in spatial representation (e.g. visual coordinates) but is easier to control in body-based representation (e.g. joint angle coordinates). The results of simulations of the model replicated both behavioral and neurophysiological findings in experimental studies using a "2/spl times/5 task".
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机译：假设神经节（BG）通过使用多巴胺神经元提供的增强信号进行增强学习。众所周知，存在多个BG-丘脑皮质环，但对其功能了解甚少。在这里，作者提出了一种计算模型，该模型使用序列的不同表示形式在粘性运动序列学习中采用了不同的BG循环。该模型的中心思想是，视觉运动序列在空间表示（例如视觉坐标）中更容易学习，但在基于身体的表示（例如关节角度坐标）中更容易控制。该模型的仿真结果使用“ 2 / spl times / 5任务”复制了实验研究中的行为和神经生理学发现。
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