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.
- Dora E Angelaki
- Jean Laurens
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Stefan Glasauer
© 2017, Laurens & Angelaki
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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