Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning
Fig 3
Tensor-component analysis (TCA) and computation of tensor score at a trial-basis.
A: TCA was conducted for PSTHs in 4 cue-response conditions of n=6,445 neurons and the resulting four tensor components (TC1-4) explained more than 50% of variance. B: for the i-th single neuron, its activity for the r-th TC (yr) in the particular j-th trial was computed by filtering spike timings with temporal profile of the r-th TC , multiplying corresponding coefficients
of the i-th neuron and
of the cue-response condition c. C-D: PSTHs (C) of two representative neurons, which have the highest coefficients of TC1 and TC2, respectively, and their TC1 and TC2 scores, respectively, computed for all trials in their corresponding sessions (D). E: Heatmaps showed TC1-4 scores averaged for all neurons in each of the eight zones distinctively for the four cue-response conditions.