How the Vestibulocerebellum Builds an Internal Model of Self-motion

Jean Laurens, Dora Angelaki

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The vestibulocerebellum transforms raw vestibular signals into central estimates of head motion. One well-studied transformation is the extraction of head tilt and translation from vestibular signals, which requires integrating signals from two different vestibular sensors: the semicircular canals and the otoliths. Here we review how tilt and translation are encoded in the vestibulocerebellum. We focus on computational aspects: we present the theoretical framework of tilt-translation discrimination, as well as the mathematical methods used to model how cerebellar neurons encode tilt and translation. We also present the experimental and statistical frameworks that allow identifying which variables are encoded by individual cerebellar neurons and probing into the underlying multisensory integration operations.

Original languageEnglish (US)
Title of host publicationThe Neuronal Codes of the Cerebellum
PublisherElsevier Inc.
Pages97-115
Number of pages19
ISBN (Print)9780128013861
DOIs
StatePublished - Oct 21 2015

Fingerprint

Head
Otolithic Membrane
Neurons
Semicircular Canals

Keywords

  • Cerebellum
  • Internal model
  • Modeling
  • Neurophysiology
  • Vestibular

ASJC Scopus subject areas

  • Medicine(all)
  • Neuroscience(all)

Cite this

Laurens, J., & Angelaki, D. (2015). How the Vestibulocerebellum Builds an Internal Model of Self-motion. In The Neuronal Codes of the Cerebellum (pp. 97-115). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-801386-1.00004-6

How the Vestibulocerebellum Builds an Internal Model of Self-motion. / Laurens, Jean; Angelaki, Dora.

The Neuronal Codes of the Cerebellum. Elsevier Inc., 2015. p. 97-115.

Research output: Chapter in Book/Report/Conference proceedingChapter

Laurens, Jean ; Angelaki, Dora. / How the Vestibulocerebellum Builds an Internal Model of Self-motion. The Neuronal Codes of the Cerebellum. Elsevier Inc., 2015. pp. 97-115
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