Integrated decoding of local and prospective spatial representations for future decision prediction

OBJECTIVE: Decoding future spatial decisions from the brain's cognitive map represents a critical step toward cognitive brain-computer interfaces (BCIs) and closed-loop neuromodulation for neurological disorders. However, accurately predicting future paths from hippocampal neural dynamics remains challenging, partly because previous studies have largely overlooked the temporal evolution of early decision-making phases.
Approach. We recorded hippocampal CA1 ensemble activity from rats performing a continuous spatial decision task in a modified T-maze. By segmenting the decision process into starting, running, and approaching phases, we examined how local and prospective spatial representations dynamically evolved over time.
Main results. Central arm place cells exhibited increasing trajectory dependence as animals approached the choice point, with local theta sequences consistently overrepresenting the actual choice. Concurrently, prospective representations driven by choice arm place cells showed a dynamic transition from preferentially predicting the actual choice during the running phase to representing potential paths more equally near the choice point. Integrating local and prospective features improved decoding performance, reaching 74.4% accuracy for future choice prediction in Test trials and 78.2% accuracy for upcoming trajectory decoding in Sample trials.
Significance. These findings demonstrate that incorporating spatiotemporal hippocampal features improves behavioral decoding and provides a framework for developing hippocampus-based BCIs to predict future decisions.