Extended Data Fig. 1: Training AutoLFADS models with Population-Based Training.

(a) Schematic of the PBT approach to HP optimization. Each colored circle represents an LFADS model with a certain HP configuration and partially filled bars represent model performance (higher is better). In our case, performance is measured by exponentially smoothed validation log-likelihood at the end of each generation. Models are trained for fixed intervals (generations), between which poorly performing models are replaced by copies of better-performing models with perturbed HPs. (b) True rate recovery performance of AutoLFADS vs. best random search model (no CD) for a given number of workers. We did not run AutoLFADS with more than 20 workers. Instead, we extrapolate with a dashed line for comparison. Random searches were simulated by drawing from the pool of runs shown in Fig. 1c. Center line denotes median and shaded regions denote upper and lower quartiles for 100 draws. (c) Hyperparameter progressions for the 20-worker AutoLFADS run shown in the previous panel. Initialization values are shown as gray points and initialization ranges are shown as gray lines.