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Related Experiment Videos

Internal models for motor control

M Kawato1, D Wolpert

  • 1ATR Human Information Processing Research Laboratories and Dynamic Brain Project, ERATO, Kyoto, Japan.

Novartis Foundation Symposium
|February 9, 1999
PubMed
Summary
This summary is machine-generated.

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The central nervous system (CNS) uses internal models to learn sensorimotor transformations for hand movements. This work reviews evidence and proposes a new computational model of multiple internal models for motor control.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Motor Control

Background:

  • Hand movement relies on complex sensorimotor transformations.
  • The central nervous system (CNS) is hypothesized to learn and maintain internal models for these transformations.
  • Internal models are neural systems that mimic the behavior of the sensorimotor system and external objects.

Purpose of the Study:

  • To review computational, behavioral, and neurophysiological evidence supporting the role of internal models in sensorimotor control.
  • To discuss the necessity, location, and learning mechanisms of internal models within the CNS.
  • To introduce a novel computational model of multiple internal models.

Main Methods:

  • Review of existing literature (computational, behavioral, neurophysiological studies).

Related Experiment Videos

  • Theoretical analysis of internal model function and acquisition.
  • Proposal of a new computational model.

    • Main Results:

    • Strong evidence supports the hypothesis that the CNS learns and maintains internal models.
    • Internal models are crucial for predicting motor command consequences and determining necessary commands.
    • A new computational model for multiple internal models is proposed.

    Conclusions:

    • Internal models are fundamental for sensorimotor transformations and motor learning.
    • The proposed multiple internal model framework offers a new perspective on motor control.
    • Further research is needed to elucidate the neural mechanisms and refine computational models.