Behavioral Geometry Guides Generalizable Neural Representation Learning

Neural recordings can change across sessions even when behavior remains similar. This makes behavior a natural reference for learning shared representations, but pointwise prediction alone does not specify how these representations should be organized. We introduce Behavioral Relational Guidance (BeRG), which uses behavioral geometry as a structural prior for neural pretraining. A frozen behavioral encoder maps behavioral histories and task context to state representations. For each state, a soft neighborhood assigns probability weights to states from other sessions of the same task, giving higher weights to more similar behavioral representations. BeRG matches these neighborhood distributions in a neural decoding subspace, jointly optimizing relational and decoding objectives. The framework supports fixed and learned encoders, while inference requires only neural inputs. We evaluate hand-velocity decoding from macaque motor-cortical recordings during center-out and random-target reaching. Across six neural architectures, BeRG reduces normalized mean squared decoding error by approximately 15–23% on validation data. Compared with fixed-feature guidance, learned guidance increases mean $R^2$ for 250-ms-ahead velocity readout from frozen representations by approximately 20%. It also improves neighborhood-based velocity readout and behavioral similarity among cross-session neighbors, with benefits in both the decoding subspace and final hidden representations. In longitudinal recordings during a finger-movement task, BeRG further improves decoding on previously unseen recording dates after few-shot calibration. These findings point to a broader role for behavioral data as structural supervision, offering a route toward neural models that remain useful across readouts and recordings.