The architecture of LFADS is described in more detail in the original publication [1]. We used a dimension of 64 for the initial condition (IC) encoder, controller input (CI) encoder, initial condition, and controller. The controller output dimension was 2 and the generator dimension was 100. The latent factor dimensionality was 40 for the maze dataset and 100 for both calcium datasets. LFADS models benefit from appropriate hyperparameter (HP) tuning, as optimal HP combinations can vary from dataset to dataset [2, 3]. As mentioned in the main text, we use AutoLFADS [3] to ensure appropriate HP tuning. The framework combines a regularization strategy (coordinated dropout; CD [2]) with a largescale framework for optimizing model hyperparameters (population-based training; PBT [4]).