Larger values of the meta-prior translate into better reconstruction of the training patterns. The four graphs show a training pattern (in orange) and its reconstruction by PV-RNN (in blue) for different values of $w$⁠. Overlapping sections are in dark gray. For $w=0.1$⁠, the target sequence is completely regenerated. When $w$ is equal to 0.025 and 0.01, all deterministic steps are correctly reproduced, but regenerated patterns begin to diverge at the 11th and 8th step, respectively. When $w$ is set to 0.0001, even the deterministic transition rules fail to be reproduced, and the signals diverge at the 6th time step.