Figure 1. Possible mechanisms of motor cortical activity changes underlying behavioral adaptation.

Hypotheses that could explain the changes in M1 spiking observed during adaptation. We assume a simplified hierarchical model where M1 responds to inputs from PMd, and its outputs drive the adapted behavior. We consider four models describing the functional connectivity within M1 (blue), functional connectivity within PMd (orange), the functional connectivity between the outputs of PMd and the M1 neurons (dark blue), or how the local PMd activity relates to M1 (green). Our experiment studies the generalization of such models during adaptation. Each inset plot shows the timecourse of changes in the behavior (gray line) and the predicted change in modeled connectivity (colored lines). We then identified four hypotheses that could explain the change in firing rates of M1: 1) the local functional connectivity could change, causing all four models to change as behavior adapts (H_local); 2) learning could arise from changes in planningrelated computations in PMd that are sent to M1 (H_plan). Here, the PMd outputs should predict changes in M1 (dark blue); 3) the mapping between PMd and M1 could change (H_map), which would not impact the within-area models (blue and orange) but would prevent the PMd to M1 models from generalizing; 4) learning could occur independently of M1 and PMd at the inputs to these areas (H_input), which would not require a change in any of the models describing this circuit.