Distributed-Cerebellar-Motor-Learning-A-Spike-Timing-Dependent-Plasticity-Model-Video2

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Learning simulation. Synaptic weight evolution during Case A 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 initial conditions started from zero whilst PC-DCN initial conditions were set to a higher value than demanded. Only one every 100 trials is shown. (Top left) 3D view of the synaptic weight distribution at PF–PC synapses. (Top right) Sagittal axis of the synaptic weight distribution at MF-DCN synapses. (Second right) The cerebellum counterbalances the existing difference between the actual cerebellar output (in red) and the reference curve (in blue) which is iteratively presented to the cerebellum over 10,000 iterations. (First and second plot of the bottom row). Evolution of the averaged gain at MF-DCN and PC-DCN synaptic weights at the first and second micro-complexes which supply an agonist (red line) or antagonist (black line) cerebellar corrective action.

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CC BY 4.0, as the Wikimedia Commons states it.

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Creative Commons Attribution 4.0

Credit

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

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