Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning
Fig 7
Licking behaviors and neural firings of the model.
A: lick rate in 500 randomly generated trials, distinguished by Go (blue dots) and No-go (red dots) cues. Each dot represents a single trial. The right panel presents mean ± std of lick rates in the first 100 trials (open bars, first stage) and the last 100 trials (filled bars, last stage). B: firing rates of IOGo and IONogo neurons in the first and last stages of the trials. C: bidirectional changes in the weight of PF-PC synapses for TCGo (cyan trace) and TCNogo (magenta trace) throughout the learning process. D: effective coupling between IOGo (left) and IONogo (right) neurons in individual trials. E-F: raster plots of IOGo (upper panel) and IONogo (lower panel) neurons in the first (E) and last (F) stages. Vertical dashed lines indicate trial (Go vs No-go) boundaries. Asterisks in A-B indicate significance level of the t-tests between the first and last stages: n.s, p < 0.05; ****, p<0.0001.