Tandem internal models execute motor learning in the cerebellum

Proc Natl Acad Sci U S A. 2018 Jul 10;115(28):7428-7433. doi: 10.1073/pnas.1716489115. Epub 2018 Jun 25.

Abstract

In performing skillful movement, humans use predictions from internal models formed by repetition learning. However, the computational organization of internal models in the brain remains unknown. Here, we demonstrate that a computational architecture employing a tandem configuration of forward and inverse internal models enables efficient motor learning in the cerebellum. The model predicted learning adaptations observed in hand-reaching experiments in humans wearing a prism lens and explained the kinetic components of these behavioral adaptations. The tandem system also predicted a form of subliminal motor learning that was experimentally validated after training intentional misses of hand targets. Patients with cerebellar degeneration disease showed behavioral impairments consistent with tandemly arranged internal models. These findings validate computational tandemization of internal models in motor control and its potential uses in more complex forms of learning and cognition.

Keywords: cerebellar degeneration; forward model; inverse model; motor control; prism adaptation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Cerebellum / pathology*
  • Female
  • Hand / physiology
  • Humans
  • Learning / physiology*
  • Male
  • Middle Aged
  • Models, Neurological*
  • Motor Activity / physiology*