Adaptive Model Theory: Modelling the Modeller
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
- 1.Neilson PD, Neilson MD, O’Dwyer NJ (1985) Acquisition of motor skills in tracking tasks: learning internal models. In: Russell DG, Abernethy B (eds) Motor memory and control: the otago symposium, Dunedin, New Zealand, 1982. Human Performance Associates, Dunedin, NZ, pp 25–36Google Scholar
- 5.Neilson PD, Neilson MD, O’Dwyer NJ (1997) Adaptive model theory: central processing in acquisition of skill. In: Connolly K, Forssberg H (eds) Neurophysiology and neuropsychology of motor development. Mac Keith Press, London, pp 346–370Google Scholar