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

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.

- Kind: Short
- Year: 2013
- Runtime: 1 min
- By: Garrido J, Luque N, D'Angelo E, Ros E
- Archive: [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Distributed-cerebellar-plasticity-implements-adaptable-gain-control-in-a-manipulation-task-a-closed-Movie1.ogv)
- Licence or status: [CC BY 3.0](https://creativecommons.org/licenses/by/3.0) (as the archive states it)
- Credit: Garrido J, Luque N, D'Angelo E, Ros E
- Who can watch: Teens profiles and older (the archive gives no rating; placed by Invisible Network from its collection and subjects)

[Watch now](https://invisiblenetwork.ai/watch/archive/commons/29053317?play=1)