Abstract
Adaptive Model Theory (AMT) is a computational theory and model of the information processing performed by the brain during voluntary movement. Two possible generalizations of AMT to the control of nonlinear external system were evaluated in a pilot study. Both approaches made use of single layer networks of locally recurrent dynamic neurons in the nonlinear inverse modeling subsystems. The feedback-error learning approach performed well in AMT tracking task simulations when compared with other approaches and human data. The forward-and-inverse learning approach performed poorly in the same tests.
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