Figure 5.

Normalized synaptic contribution of each DCN afferent throughout the learning process evolution using a self-adaptable IO-DCN connection. Simulations were performed using plasticity mechanisms at PF-PC, MF-DCN, and PC-DCN synapses using a self-adaptive plasticity mechanism at IO-DCN connection for manipulating 2 and 10 kg external payloads during 5000 trials. The evolution of the average MAEs of the three robot joints during the learning process for 2 kg (A) and 10 kg payloads (B) with a cerebellum equipped with IO-DCN connection with/without self-adaptive synaptic weights is presented. The initial cerebellar system gain was properly set to operate with no payload. Since the manipulated masses were unexpected, the existing plasticity mechanisms at MF-DCN and PC-DCN adjusted the cerebellar output to cope with these masses. At initial learning stages, the cerebellar model presenting an adjustable IO-DCN connection provided a more accurate corrective action to properly perform the manipulation task. The distributed adaptation of IO-DCN, MF-DCN, and PC-DCN synaptic strengths when using 2 kg (A) and 10 kg payloads (B) related to join 2 agonist muscle is also presented. 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. The self-adjustable IO-DCN connection was capable of supplying a proper adjustment from almost the beginning of the learning process. The control action of this connection was relevant only in early learning stages; once the learning process settled down, the IO control action became negligible (see zoom-in of normalized synaptic weight evolution plots).