Generalization as a behavioral window to the neural mechanisms of learning internal models
Generalization as a behavioral window to the neural mechanisms of learning internal models
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DOI：
10.1016/j.humov.2004.04.003
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发表时间：
2004-11-01
影响因子：
2.1
通讯作者：
Shadmehr, R
中科院分区：
文献类型：
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作者：
Shadmehr, R
在产生运动指令时，大脑似乎依赖于预测肢体和外部世界的物理动力学的内部模型。大脑如何计算内部模型？涉及哪些神经结构？我们考虑一项任务，其中力场施加到手，改变了达到的物理动力学。行为测量表明，当大脑适应磁场时，它会将手臂的期望感觉状态映射到对力的估计。如果这种神经计算是通过人口代码来执行的，即，通过一组基，则基的活动字段指示泛化功能，该泛化功能使用在给定状态中经历的错误来影响在任何其它状态中的性能。泛化的模式表明，基地的活动领域，定向调谐，但定向调谐可能是双峰。肢体位置以及上下文线索成倍地调节调谐的增益。这些特性与运动皮层和小脑中细胞的活动区域一致。我们认为，在这些运动区域的细胞的活动领域决定了我们代表肢体动力学的内部模型的方式。(C)2004 Elsevier B.V.保留所有权利。
In generating motor commands, the brain seems to rely on internal models that predict physical dynamics of the limb and the external world. How does the brain compute an internal model? Which neural structures are involved? We consider a task where a force field is applied to the hand, altering the physical dynamics of reaching. Behavioral measures suggest that as the brain adapts to the field, it maps desired sensory states of the arm into estimates of force. If this neural computation is performed via a population code, i.e., via a set of bases, then activity fields of the bases dictate a generalization function that uses errors experienced in a given state to influence performance in any other state. The patterns of generalization suggest that the bases have activity fields that are directionally tuned, but directional tuning may be bimodal. Limb positions as well as contextual cues multiplicatively modulate the gain of tuning. These properties are consistent with the activity fields of cells in the motor cortex and the cerebellum. We suggest that activity fields of cells in these motor regions dictate the way we represent internal models of limb dynamics. (C) 2004 Elsevier B.V. All rights reserved.