FreeCAB: A Benchmark for Neural Encoding and Decoding from Freely Moving Hippocampal Calcium Imaging

Neural encoding and decoding are commonly evaluated in constrained or trial-structured settings with curated targets and relatively homogeneous recordings, leaving continuous neural–behavior modeling under freely moving conditions less systematically characterized. We introduce FreeCAB, a benchmark built from 58 curated session-level recordings that pair dorsal CA1 miniscope calcium imaging with synchronized multi-view behavior. FreeCAB defines matched bidirectional sequence tasks under a unified session-based interface, evaluating both 2D position and reconstructed 3D pose in single-session neural encoding and decoding, together with explicit tracking-quality control. Position-based extensions further evaluate behavioral-state dependence, held-out adaptation after multi-session pretraining, and joint encoding–decoding training. Across five general-purpose baselines, position decoding is consistently more reliable than position-to-neural encoding. Using 3D pose as the encoding input yields higher Pearson correlation than 2D position for all five baselines, although explained variance does not improve uniformly; conversely, 3D pose remains decodable from neural activity but is more difficult to decode than 2D position. Position decoding is also consistently stronger during locomotion across tested speed thresholds. Multi-session pretraining and joint training produce model-, metric-, and task-dependent effects rather than uniformly reliable gains. Together, FreeCAB provides a benchmark for studying how behavioral representation, behavioral state, and session heterogeneity shape neural–behavior modeling under continuous free behavior.