We suggest that the parallel circuit architecture of the BG might be expected to produce variability in the response properties of different dopamine neurons, and that variability in response profile may not reflect variable functions, but rather different arguments that serve as inputs to a common function: the computation of prediction error.
Reinforcement-learning models rely on the computation of prediction errors (i.e., the difference between the expected and received rewards), which are used to inform subsequent actions and maximize reward over time (O'Doherty, 2004; Pessiglione, Seymour, Flandin, Dolan, & Frith, 2006; Schönberg, Daw, Joel, & O'Doherty, 2007; Sutton & Barto, 1998).
These findings establish that 2 classes of repetition responses coexist within 1 visual region and support models acknowledging this distinction, such as predictive coding models where perception requires the computation of both predictions (which are enhanced by repetition) and prediction errors (which are suppressed by repetition).
Still, existing methods would take ∼29 CPU years to complete large-scale studies such as the computation of contact predictions for the 30% of Pfam with sufficient sequence coverage (see Supplementary Section 7).
A simulation is the computation of a prediction from a model, but there is no computer file that holds a model for protein dynamics, and another file that holds a simulation program.
Therefore, a more efficient algorithm that reduces the computation of inter prediction is highly desirable.
CPSign allows the computation of a "prediction gradient", as described in [29].
The computation of the prediction uncertainty accounts for the errors associated to both the estimations of i) SOC stocks and ii) parameters of the spatial model.
