Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Exp Brain Res. 2011 May;210(3-4):407-22. doi: 10.1007/s00221-011-2568-4. Epub 2011 Feb 4.
Research in the vestibular field has revealed the existence of a central process, called 'velocity storage', that is activated by both visual and vestibular rotation cues and is modified by gravity, but whose functional relevance during natural motion has often been questioned. In this review, we explore spatial orientation in the context of a Bayesian model of vestibular information processing. In this framework, deficiencies/ambiguities in the peripheral vestibular sensors are compensated for by central processing to more accurately estimate rotation velocity, orientation relative to gravity, and inertial motion. First, an inverse model of semicircular canal dynamics is used to reconstruct rotation velocity by integrating canal signals over time. However, its low-frequency bandwidth is limited to avoid accumulation of noise in the integrator. A second internal model uses this reconstructed rotation velocity to compute an internal estimate of tilt and inertial acceleration. The bandwidth of this second internal model is also restricted at low frequencies to avoid noise accumulation and drift of the tilt/translation estimator over time. As a result, low-frequency translation can be erroneously misinterpreted as tilt. The time constants of these two integrators (internal models) can be conceptualized as two Bayesian priors of zero rotation velocity and zero linear acceleration, respectively. The model replicates empirical observations like 'velocity storage' and 'frequency segregation' and explains spatial orientation (e.g., 'somatogravic') illusions. Importantly, the functional significance of this network, including velocity storage, is found during short-lasting, natural head movements, rather than at low frequencies with which it has been traditionally studied.
前庭领域的研究揭示了一种中央过程的存在，称为“速度存储”，它既可以被视觉和前庭旋转线索激活，也可以被重力改变，但它在自然运动中的功能相关性经常受到质疑。在这篇综述中，我们在前庭信息处理的贝叶斯模型的背景下探讨了空间定向。在这个框架中，通过中央处理来补偿外周前庭传感器的缺陷/歧义，以更准确地估计旋转速度、相对于重力的方向和惯性运动。首先，使用半规管动力学的逆模型通过随时间对通道信号进行积分来重建旋转速度。然而，它的低频带宽受到限制，以避免积分器中噪声的积累。第二个内部模型使用这个重建的旋转速度来计算倾斜和惯性加速度的内部估计。这个第二个内部模型的带宽也在低频受到限制，以避免噪声积累和倾斜/平移估计器随时间的漂移。因此，低频平移可能会被错误地误解为倾斜。这两个积分器（内部模型）的时间常数可以分别被概念化为零旋转速度和零线性加速度的两个贝叶斯先验。该模型复制了经验观察，如“速度存储”和“频率分离”，并解释了空间定向（例如，“体感重力”）错觉。重要的是，这个网络的功能意义，包括速度存储，是在短暂的、自然的头部运动中发现的，而不是在传统上研究的低频范围内。
