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Generalization in vision and motor control

Author

Listed:
  • Tomaso Poggio

    (McGovern Institute, Center for Biological and Computational Learning, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology)

  • Emilio Bizzi

    (McGovern Institute, Center for Biological and Computational Learning, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
    European Brain Research Institute, Via del Fosso di Fiorano)

Abstract

Learning is more than memory. It is not simply the building of a look-up table of labelled images, or a phone-directory-like list of motor acts and the corresponding sequences of muscle activation. Central to learning and intelligence is the ability to predict, that is, to generalize to new situations, beyond the memory of specific examples. The key to generalization, in turn, is the architecture of the system, more than the rules of synaptic plasticity. We propose a specific architecture for generalization for both the motor and the visual systems, and argue for a canonical microcircuit underlying visual and motor learning.

Suggested Citation

  • Tomaso Poggio & Emilio Bizzi, 2004. "Generalization in vision and motor control," Nature, Nature, vol. 431(7010), pages 768-774, October.
    • Handle: RePEc:nat:nature:v:431:y:2004:i:7010:d:10.1038_nature03014
    DOI: 10.1038/nature03014
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    Cited by:

    1. Yael Mandelblat-Cerf & Itai Novick & Eilon Vaadia, 2011. "Expressions of Multiple Neuronal Dynamics during Sensorimotor Learning in the Motor Cortex of Behaving Monkeys," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-14, July.
    2. Hagai Lalazar & L F Abbott & Eilon Vaadia, 2016. "Tuning Curves for Arm Posture Control in Motor Cortex Are Consistent with Random Connectivity," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-27, May.
    3. Taisei Sugiyama & Nicolas Schweighofer & Jun Izawa, 2023. "Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. Lindkvist, Emilie & Norberg, Jon, 2014. "Modeling experiential learning: The challenges posed by threshold dynamics for sustainable renewable resource management," Ecological Economics, Elsevier, vol. 104(C), pages 107-118.

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