A unified internal model theory to resolve the paradox of active versus passive self-motion sensation
A unified internal model theory to resolve the paradox of active versus passive self-motion sensation
复制标题
DOI：
10.7554/elife.28074
复制
发表时间：
2017-10-18
期刊：
影响因子：
7.7
通讯作者：
Angelaki, Dora E.
中科院分区：
文献类型：
--
作者：
Laurens, Jean;Angelaki, Dora E.
脑干和小脑神经元实现了一个内部模型，以准确地估计外部生成（“被动”）运动期间的自我运动。然而，这些神经元在自发（“主动”）运动期间表现出减少的反应，表明运动命令的预测感觉结果取消了感觉信号。值得注意的是，在主动运动过程中的感觉预测的计算过程及其与被动运动过程中的内部模型计算的关系仍然未知。我们构建了一个卡尔曼滤波器，将电机命令到先前建立的模型的最佳被动自运动估计。模拟的感觉误差和反馈信号与主动和被动头部和躯干旋转和平移期间实验测量的神经元反应相匹配。我们的结论是，一个单一的感觉内部模型可以联合收割机运动指令与前庭和本体感受信号的最佳组合。因此，尽管携带感觉预测误差或反馈信号的神经元显示衰减的调制，但感觉线索和内部模型对于主动头部运动期间的准确自我运动估计都是至关重要的。
Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally generated ('passive') movements. However, these neurons show reduced responses during self-generated ('active') movements, indicating that predicted sensory consequences of motor commands cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations during passive movements remain unknown. We construct a Kalman filter that incorporates motor commands into a previously established model of optimal passive self-motion estimation. The simulated sensory error and feedback signals match experimentally measured neuronal responses during active and passive head and trunk rotations and translations. We conclude that a single sensory internal model can combine motor commands with vestibular and proprioceptive signals optimally. Thus, although neurons carrying sensory prediction error or feedback signals show attenuated modulation, the sensory cues and internal model are both engaged and critically important for accurate self-motion estimation during active head movements.