Models that contain the Implementer : Crevecoeur, Frédéric

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    Models   Description
1.  Reaching movements with robust or stochastic optimal control models (Crevecoeur et al 2019)
"We explored the hypothesis that compensation for unmodelled disturbances was supported by a robust neural control strategy. We studied the predictions of stochastic optimal control (LQG) (Linear Quadratic Gaussian) (Todorov, 2005) and a robust control design that can equivalently be described as a “min-max” or worst-case strategy (Basar and Bernhard, 1991) applied to linear models of planar reaching movements. The robust controller displayed an increase in control gains, resulting in faster movements towards the target and more vigorous responses to perturbations. Our experimental results supported these predictions: the occurrence of unexpected force field disturbances evoked both faster movements and more vigorous responses to perturbations. Thus, the neural controller was more robust in the sense that the feedback responses reduced the impact of the perturbations (step and force field). Thus the compensation for disturbances involved a “model-free” component. ..."
2.  Within movement adjustments of internal representations during reaching (Crevecoeur et al 2020)
"An important function of the nervous system is to adapt motor commands in anticipation of predictable disturbances, which supports motor learning when we move in novel environments such as force fields (FFs). Here, we show that movement control when exposed to unpredictable disturbances exhibit similar traits: motor corrections become tuned to the FF, and they evoke after effects within an ongoing sequence of movements. We propose and discuss the framework of adaptive control to explain these results: a real-time learning algorithm, which complements feedback control in the presence of model errors. This candidate model potentially links movement control and trial-by-trial adaptation of motor commands."

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