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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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MAE and synaptic strength evolution during the learning process at IO-DCN, MF-DCN, and PC-DCN synapses when an external variable force is applied to the end effector. Simulations were performed using plasticity mechanisms at PF-PC, MF-DCN, and PC-DCN synapses accompanied with a self-adaptive plasticity mechanism at IO-DCN connection under an external variable force field during 5000 trials. (A) Illustrates the mean absolute error evolution of the three robot joints during the learning process throughout 5000 trials accompanied with a zoom-in of the first 500 trials with/without the self-adaptive plasticity mechanism at IO-DCN synapses. Plot (A) also illustrates the distributed adaptation of the normalized synaptic strength evolution at MF-DCN, PC-DCN, and IO-DCN connections (for the sake of clarity just second agonist and antagonist paired joint muscle have been represented). The contribution of the IO-DCN (red line) connection from the 1st trial was maximal; but the agonist/antagonist balance was not properly settled down. Agonist IO-DCN connection supplied a position-error-base- control action, thus facilitating the proper PF-PC firing range operation. The combined contribution of MF-DCN/PC-DCN connections (green and blue lines) became strong enough from 500th trial to keep the system under control allowing a fine-tuning of agonist/antagonist balance whilst the IO-DCN contribution was progressively self-neglected. (B) Perturbing torque values resulting from force field action compared to desired torque values needed to perform the eight-like trajectory (C) Torque value evolution during the learning process for the second joint with/without the self-adaptive plasticity mechanism at IO-DCN synapses. Three time stamps were shown: 1, 500, and 1500 trial. IO-DCN contribution was responsible for correcting the initial torque output in a rough manner (first trial). With the passage of time the fine agonist/antagonist balance at MF-DCN/PC-DCN connections allowed the arm-robot-system to compensate torque deviations due to force field action.