Adaptive Model Theory: Modelling the Modeller
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Adaptive Model Theory is a computational theory of the brain processes that control purposive coordinated human movement. It sets out a feedforward-feedback optimal control system that employs both forward and inverse adaptive models of (i) muscles and their reflex systems, (ii) biomechanical loads on muscles, and (iii) the external world with which the body interacts. From a computational perspective, formation of these adaptive models presents a major challenge. All three systems are high dimensional, multiple input, multiple output, redundant, time-varying, nonlinear and dynamic. The use of Volterra or Wiener kernel modelling is prohibited because the resulting huge number of parameters is not feasible in a neural implementation. Nevertheless, it is well demonstrated behaviourally that the nervous system does form adaptive models of these systems that are memorized, selected and switched according to task. Adaptive Model Theory describes biologically realistic processes using neural adaptive filters that provide solutions to the above modelling challenges. In so doing we seek to model the supreme modeller that is the human brain.
KeywordsAdaptive nonlinear models Neural adaptive filters Feature extraction Movement synergies Riemannian geometry
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