Distributed-cerebellar-plasticity-implements-adaptable-gain-control-in-a-manipulation-task-a-closed-Movie1

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Learning simulation, joint-1-related activity and weight evolution during manipulation of a 10-kg load. Simulations were carried out using all the plasticity mechanisms (PF-PC, MF-DCN, and PC-DCN) along 1000 trials. Only 1 every 10 trials is shown. The movement has been recorded in real-time (each trial lasts 1 s) evidencing the difficulty of the task. (top left) 3D view of the actual (black) and desired (red) robot end-effector trajectory in Cartesian coordinates. (medium left) Ideal (dotted lines) and actual (solid lines) corrective torques during the current trial for joint 1 (blue), 2 (red), and 3 (green). (bottom left) Evolution of the MAE. (top right) Evolution of four randomly chosen PF-PC synaptic weights. (second to fifth rows right) Evolution of PC activity, DCN activity, MF-DCN, and PC-DCN synaptic weights related to joint 1 agonist (solid line) and antagonist (dotted line) muscles. Note that, at the end of learning, joint-1 DCN neurons provided higher corrective torques to the antagonist muscle during the first half of the trial and to the agonist muscle during the second half of the trial.

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Credit

Garrido J, Luque N, D'Angelo E, Ros E

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