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File:Distributed-Cerebellar-Motor-Learning-A-Spike-Timing-Dependent-Plasticity-Model-Video1.ogv

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English: 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 6000 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) 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 5000 iterations. (First and second plot of the bottom row). Evolution of the averaged gain at MF-DCN and PC-DCN synaptic weights at first and second micro-complexes which supply agonist (red line) or antagonist (black line) cerebellar corrective actions.
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Source Movie S1 from Luque N, Garrido J, Naveros F, Carrillo R, D'Angelo E, Ros E (2016). "Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model". Frontiers in Computational Neuroscience. DOI:10.3389/fncom.2016.00017. PMC: 4773604.
Author Luque N, Garrido J, Naveros F, Carrillo R, D'Angelo E, Ros E
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VP9 720P 445 kbps Completed 13:58, 24 August 2018 29 s
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