Learning Predictive Representations of Neural Population Dynamics

Neural interfaces and population analyses increasingly require representations that can support several uses without learning a new neural encoder for each task. A central challenge is to learn such states from neural activity itself while retaining information about how the population evolves. We introduce Neural Predictive Coding (NPC), a self-supervised framework that uses future population activity as the learning signal. A causal encoder summarizes past and present activity into a compact state and contrastively predicts learned future targets across multiple timescales. Across motor control, spatial navigation, visual perception, and cognitive timing in three species, frozen NPC states improve forecasts of neurons excluded from pretraining when added to recent-activity summaries. These states also permit task decoding across all four functions, including mean $R^2$ of 0.50 for motor velocity and 0.58 for spatial position. Task-associated trajectories admit fitted updates that improve neural forecasts over static states in every function; in vision, evolved NPC states yield a 60% higher mean likelihood-gain score than CEBRA-Time. In a separate source-disjoint mouse cohort, pretraining improves likelihood-gain scores by 7% over scratch training at matched target updates and full query coverage, although longer scratch training performs better. Future-neural prediction thus provides a common training task for reusable readouts and quantitative analysis of population-state evolution.