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. 2014 Aug 15;8:97. doi: 10.3389/fncom.2014.00097

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Copyright © 2014 Luque, Garrido, Carrillo, D'Angelo and Ros.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Learning generalization by means of distributed plasticity. The system gain (external to the cerebellum) was properly set to manipulate accurately the robot-arm without any object (no external payload). Since the manipulated mass (payload) was not expected, the existing plasticity mechanisms at MF-DCN and PC-DCN had to adjust the cerebellar output to cope with this mass (2 kg/10 kg mass configuration). (A1) Performance and learning when manipulating 2 kg mass. Evolution of the average MAE of the three robot joints during the learning process, 5000 trials. In the initial learning trials (zoom in) the MAE averaged value was about 10 times greater than the obtained MAE average value at the end of the learning process. MF-DCN and PC-DCN adjustments took about 500 iterations to be set, meanwhile the cerebellar system was working in open-loop and no action control was appropriately delivered. Plasticity occurred at PF-PC, MF-DCN, and PC-DCN synapses. The evolution of synaptic weights at MF-DCN, PC-DCN connections related to join 2 agonist muscle is also shown. For the sake of clarity only the behavior of this second joint is shown, however similar results were found throughout the learning process in both joints 1 and 3. MF-DCN and PC-DCN synaptic weight stabilization was obtained from the 500th trial. (A2) Normalized PC Firing rate (top) and DCN firing rate (bottom) during different trials taken from the initial stages of the learning process: trial 1, trial 250, and trial 500. MF-DCN and PC-DCN synaptic weight adjustments allowed the PC/DCN firing rate to operate in a proper range. (B1) Performance and learning when manipulating 10 kg mass. Evolution of the average MAE of the three robot joints during the learning process, 5000 trials. In the initial learning trials (zoom in) the MAE averaged value was, roughly speaking, more than 30 times greater than the obtained MAE average value at the end of the learning process. MF-DCN and PC-DCN adjustments took about 1000 iterations to settle down, meanwhile the cerebellar system was working in open-loop, and hence no action control was appropriately delivered. Plasticity occurred at PF-PC, MF-DCN, and PC-DCN synapses. The evolution of synaptic weights at MF-DCN, PC-DCN connections related to join 2 agonist muscle is also shown. For the sake of clarity only the behavior of this second joint is shown, however similar results were found throughout the learning process in both joints 1 and 3. MF-DCN and PC-DCN synaptic weight stabilization was obtained from the 3000th trial. (B2) Normalized PC firing rate (top) and DCN firing rate (bottom) during different trials taken from the initial stages of the learning process: trial 1, trial 500, and trial 1000. MF-DCN and PC-DCN synaptic weight adjustments allowed the PC/DCN firing rate to operate in a proper range.