Extended Data Fig. 9: Classifying neurons with BIC instead of AIC.
From: A quantitative reward prediction error signal in the ventral pallidum
(a) Fraction of neurons classified as RPE, Current outcome, and Unmodulated in VP and NAc in the random sucrose/maltodextrin task using Bayesian information criterion (BIC) as the selection criterion. (b) Coefficients(+/−SE) for outcome history regression for VP neurons of each BIC subset (n = 37 RPE, 110 Current outcome, and 289 Unmodulated cells from 5 rats). (c) Population mean(+/−SEM) of all VP BIC RPE neurons, binned according to the model-derived RPE. (d) Mean(+/−SEM) population activity of simulated and actual BIC RPE neurons according to each trial’s RPE value for VP (left) and NAc (right). (e) Distribution of correlations between model-predicted and actual spiking for all RPE neurons from each region. (f) Distribution of α for RPE neurons in VP (green) and NAc (orange). (g) Mean(+/−SEM) activity of VP neurons classified as RPE by AIC but not BIC according to current and previous outcome (n = 35 neurons). (h) Coefficients(+/−SE) for outcome history regression for these neurons. (i) Mean(+/−SEM) activity of these neurons binned according to model-derived RPE on each trial.
