Distributed-Cerebellar-Motor-Learning-A-Spike-Timing-Dependent-Plasticity-Model-Video3

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Learning simulation. Synaptic weight evolution during Case B study is plotted. Simulations were run using plasticity mechanisms at PF–PC, MF-DCN, and PC-DCN along 10,000 trials. PF–PC synaptic conductances were set to an initial value of 5 nS, MF-DCN and PC-DCN initial conditions started from zero. Only one every 100 trials is shown. (Top left) Sagittal axis 3D view of the synaptic weight distribution at PF–PC synapses. (Top right) Sagittal axis 3D view of the synaptic weight distribution at MF-DCN synapses. (Second row plots) Evolution of the averaged gains at MF-DCN synaptic weights from the first to sixth micro-complex. Each micro-complex supplies an agonist (red line) or antagonist (black line) cerebellar corrective action in each robot joint. The error curve to be corrected is the difference between controlled (robot actual position and velocities) and reference variables (desired position and velocities). (Third row plots) Evolution of the averaged gain at PC-DCN synaptic weights from the first to sixth micro-complexes. Each micro-complex supplies an agonist (red line) or antagonist (black line) cerebellar corrective action at its corresponding robot joint.

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Credit

Luque N, Garrido J, Naveros F, Carrillo R, D'Angelo E, Ros E

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