A unified internal model theory to resolve the paradox of active versus passive self-motion sensation.

Jean Laurens,Dora E Angelaki

doi:10.7554/elife.28074

Abstract

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.

Highlights

For many decades, research on vestibular function has used passive motion stimuli generated by rotating chairs, motion platforms or centrifuges to characterize the responses of the vestibular motion sensors in the inner ear and the subsequent stages of neuronal processing
We show how the same internal model may process both active and passive motion stimuli and provide quantitative simulations that reproduce a wide range of behavioral and neuronal responses
A Kalman filter (Kalman 1960) is based on a forward model of a dynamical system, defined by a set of state variables X that are driven by their own dynamics, motor commands Xu and internal or external aCC-BY 4.0 International license

Summary

Introduction

Research on vestibular function has used passive motion stimuli generated by rotating chairs, motion platforms or centrifuges to characterize the responses of the vestibular motion sensors in the inner ear and the subsequent stages of neuronal processing. This research has revealed elegant computations where the brain uses an internal model to overcome the dynamic limitations and ambiguities of the vestibular sensors (Mayne 1974; Oman 1982; Borah et al 1988; Merfeld 1995; Zupan and Merfeld, 2002; Laurens 2006; Laurens and Droulez 2007, 2008; Laurens and Angelaki 2011; Karmali and Merfeld 2012; Lim et al 2017) Neuronal correlates of these computations have been identified in brainstem and cerebellum (Angelaki et al 2004; Shaikh et al 2005; Yakusheva et al 2007, 2008, 2013, Laurens et al 2013a,b). Elegant experiments by Brooks, Cullen and colleagues (Brooks et al, 2015; Brooks and Cullen, 2015; Cullen and Brooks, 2015) have provided strong evidence that the brain predicts how self-generated motion

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