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Neural basis of optimal motor control

Principal investigator(s): Pierre Morel and Sophie Denève

Several behavioral experiments suggest that the nervous system use an internal model of the dynamics of the body to implement a close approximation to a Bayesian filter. This filter can be used to perform a variety of tasks near optimally such as predicting the sensory consequence of motor action, integrating sensory and body posture signals, and computing motor commands. We propose that the neural implementation of this Bayesian filter involves recurrent basis function networks with attractor dynamics, a kind of architecture that can be readily mapped onto cortical circuits. In such networks, the tuning curves to variables such as arm velocity are remarkably non invariant in the sense that the amplitude and width of the tuning curves of a given neuron can vary greatly depending on other variables such as the position of the arm, or the reliability of the sensory feedback. This property could explain some puzzling properties of tuning curves in the motor and premotor cortex, and leads to several new predictions.

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