Uncertainty–guided learning with scaled prediction errors in the basal ganglia
Fig 4
Plasticity and computations in the basal ganglia circuit.
A The nonlinear transformation of dopaminergic prediction errors in the SPE model. The transformation in the direct pathway (i) and the transformation in the indirect pathway (ii) are mirror images of each other. B We plot the proportion of occupied receptors in the striatum as a function of dopamine concentration. The curves are based on the results of Dreyer, Herrik [26]. The blue vertical lines indicate the baseline dopamine concentration in the ventral striatum, based on the results of Dodson, Dreyer [27]. The green curve corresponds to the occupancy of D1 receptors, the red curve corresponds to the occupancy of D2 receptors. Panel B is adapted from figure 3D of Möller and Bogacz [23]. C The connectivity underlying a dynamical model of the simplified basal ganglia circuit. Circles correspond to neural populations; arrows between them indicate connections. D The computation of a scaled prediction error in continuous time, according to a dynamical model of the basal ganglia. We show how the relevant variables, T and δ, evolve as a function of time, assuming a step–function activation for the input nodes G, N and r. The black line in the lowest panel indicates the level of dopamine required for exact SPE learning.