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BMC Neuroscience

Open Access

Learning sensitivity derivative by implicit supervision

  • Mohamed N Abdelghani1Email author,
  • Timothy P Lillicrap4 and
  • Douglas B Tweed1, 2, 3
BMC Neuroscience20078(Suppl 2):P201

Published: 6 July 2007

In control theory, variables called sensitivity derivatives quantify how a system's performance depends on the commands from its controller. Knowledge of these derivatives is a prerequisite for adaptive control, including sensorimotor learning in the brain, but no one has explained how the derivatives themselves could be learned by real neural networks, and some say they aren't learned at all but are known innately. Here we show that this knowledge can't be solely innate, given the adaptive flexibility of neural systems. And we show how it could be learned using forms of information transport available in the brain. The mechanism, which we call implicit supervision, explains how sensorimotor systems cope with high-dimensional workspaces, tools, and other task complexities. It accelerates learning and explains a wide range of findings on the limits of adaptability, which are inexplicable by any theory that relies solely on innate knowledge of the sensitivity derivatives.

Authors’ Affiliations

Department of Physiology, University of Toronto
Department of Medicine, University of Toronto
Centre for Vision Research, York University
Centre for Neuroscience Studies, Queen's University


© Abdelghani et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd.