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Computational principles of movement neuroscience

Nature Neuroscience volume 3, pages 1212–1217 (2000)Cite this article

Abstract

Unifying principles of movement have emerged from the computational study of motor control. We review several of these principles and show how they apply to processes such as motor planning, control, estimation, prediction and learning. Our goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.

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Figure 1: The sensorimotor loop, showing motor command generation (top), state transition (right) and sensory feedback generation (left).
Figure 2: Task optimization in the presence of signal-dependent noise (TOPS) model of Harris and Wolpert9.
Figure 3: A schematic of one step of a Kalman filter model recursively estimating the finger's location during a movement.
Figure 4: A schematic of context estimation with just two contexts, that a milk carton is empty or full.
Figure 5: A schematic of feedback-error learning.

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Acknowledgements

We thank Pierre Baraduc, Robert van Beers, James Ingram, Kelvin Jones and Philipp Vetter for comments on the manuscript. This work was supported by grants from the Wellcome Trust, the Gatsby Charitable Foundation and the Human Frontiers Science Organization.

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Authors and Affiliations

  1. Sobell Department of Neurophysiology, Institute of Neurology, Queen Square, University College London, London, WC1N 3BG, UK

    Daniel M. Wolpert

  2. Gatsby Computational Neuroscience Unit, Queen Square, University College London, London, WC1N 3AR, UK

    Zoubin Ghahramani

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  1. Daniel M. Wolpert
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Correspondence to Daniel M. Wolpert.

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Wolpert, D., Ghahramani, Z. Computational principles of movement neuroscience. Nat Neurosci 3 (Suppl 11), 1212–1217 (2000). https://doi.org/10.1038/81497

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