Different solutions for networks trained on a sine wave task. All networks have N = 512 neurons. Four regimes: A: aligned for small output weights, B: marginal for large output weights, small recurrent weights, C: lazy for both large output and recurrent weights, D: oblique for large output weights and noise added during training. Left: Output (dark), target (purple dots), and four states (light) of the network after training. Black bars indicate the scales for output and states (length = 1; same for all regimes). The output beyond the target interval t ∈ [1, 21] can be considered as extrapolation. The network in the oblique regime, D, receives white noise during training, and the evaluation is shown with the same noise. Without noise, this network still produces a sine wave (not shown). Right: Projection of states on the first 2 PCs and the orthogonal component wout,⊥ of the output vector. All axes have the same scale, which allows for comparison between the dynamics. Vectors show the (amplified) output weights, dotted lines the projection on the PCs (not visible for lazy and oblique). The insets for the marginal solution (B, left and right) show the dynamics magnified by .