Figure 4.

Weight evolution in the cerebellar model manipulating different payloads with IO-DCN connection operating with multiple plasticity mechanisms. Simulations were performed using plasticity mechanisms at PF-PC, MF-DCN, and PC-DCN synapses using a custom-configured IO-DCN connection for manipulating 2 and 10 kg external payloads during 5000 trials. The initial cerebellar system gain was properly set to operate with no payload. (A1,B1) Evolution of the average MAEs of the three robot joints during the learning process for 2 and 10 kg payloads respectively with/without IO-DCN fixed synaptic weights plus cerebellum or with just the IO-DCN connection. Note that the configuration without IO-DCN connection adjusted the DCN gain after approximately 500/1000 (2 kg/10 kg configuration) trials on average. From the first trial to the 500th/1000th (2 kg/10 kg configuration) the cerebellar system worked almost in open loop, no remarkable corrective action was applied by the cerebellar adapting system. The configurations with or just IO-DCN connection were capable of supplying a proper adjustment from the beginning of the learning process. (A2,B2) Evolution of synaptic weights at IO-DCN, MF-DCN, and PC-DCN connections related to join 2 agonist muscle. For the sake of clarity only the behavior of this second joint is shown, however similar results were found along the learning process in both joints 1 and 3. MF-DCN and PC-DCN weights stabilized in about 500/3000 trials (2 kg/10 kg configuration) at different convergence speeds. This slow convergence was the consequence of the existing inter-dependence between the PC-DCN learning and the DCN activity which also depended on both MF-DCN and PC-DCN adaptation. IO-DCN connection supplied cerebellar control action whilst MF-DCN and PC-DCN synaptic weights were not stable yet.