Extended Data Fig. 3: Boundary vector cell analysis.

a, Schematic of the boundary vector cell (BVC) model, adapted from Lever et al.³³. A BVC reaches the highest firing rate when the animal is at its preferred firing orientation and distance to the wall. b, Schematic of the experiment to test for BVCs in the larval zebrafish brain (Methods). A wall hidden in the middle of the rectangular chamber is inserted between the 1^st session (baseline) and the 2^nd session of the experiment. Then the wall is removed again for the recording of the 3^rd session of the experiment. The BVC model predicts that a BVC with a preferred horizontal firing orientation would duplicate its firing field when a new wall is inserted. c, Procedure for identification of boundary vector cells. First, we identified suitable candidate cells in the telencephalon with PFs parallel to the inserted wall (which could be PCs or non-PCs, left). We then fit the BVC model to the spatial activity maps of these candidate cells in session 1 and used the fitted model to generate predicted activity maps across sessions (with or without wall insertion). We compare the actual spatial activity maps (left) and the corresponding map predicted by the BVC model (right) to evaluate whether each cell is consistent with the BVC model. For each telencephalic candidate cell, the model is fit to the baseline (1^st session) and then used to predict the spatial activity map for the case of wall insertion or removal (2^nd and 3^rd sessions, Methods). Top right shows an example neuron that is consistent with the BVC model prediction. Bottom right shows an example of a neuron that does not follow the BVC model prediction. d, Additional examples of neurons that follow and do not follow the BVC model prediction.