Abstract
Traditional neural decoders link neural activity to behavior within single trials of a session, overlooking correlations across trials and sessions. However, animals show similar neural patterns when performing the same task, and their behaviors are influenced by prior experiences. To capture these dependencies, we introduce two complementary models: a multi-session reduced-rank regression model that shares behaviorally relevant neural structure across sessions and a multi-session state-space model that captures behavioral structure across trials and sessions. On 433 sessions spanning 270 brain regions in the International Brain Laboratory (IBL) mouse Neuropixels dataset, our decoders outperform traditional approaches on four behaviors, with results generalizing across datasets, species, and tasks. Unlike deep learning methods, our models are efficient and interpretable, providing low-dimensional neural representations, task-related single-neuron contributions, and brain-wide timescales of neural activation.