Fig. 1: RSC represents spatial information conjunctively with hypothesis states during navigation with locally ambiguous landmarks.

a, Two perceptually identical landmarks are visible only from close up, and their identity is defined only by their relative location. One of 16 ports, at landmark ‘b,’ delivers reward in response to a nose-poke. The animal must infer which of the two landmarks is ‘b’ to receive reward; wrong pokes result in timeout. Tetrode array recordings in RSC yield 50–90 simultaneous neurons. b, Top, schematic example trial; bottom, best possible guesses of the mouse position. LM0, LM1 and LM2 denote task phases when the mouse has seen zero, one or two landmarks and could infer their position with decreasing uncertainty. c, Left, example training curve showing P_hit/P_{false-positive}; random chance level is 1/16 for 16 ports. Mice learned the task at values >1, showing they could disambiguate between the two sequentially visible landmarks. This requires the formation, maintenance and use of spatial hypotheses. Asterisks denote per-session binomial 95% significance for the correct rate. Right, summary statistics show binomial CIs on last half of sessions for all four mice. d, Mouse location heatmap from one session (red) with corresponding spatial firing rate profiles for five example cells; color maps are normalized per cell. e, Task phase (corresponding to hypothesis states in b can be decoded from RSC firing rates. Horizontal line, mean; gray shaded box, 95% CI. f, Spatial coding changes between LM1 and LM2 phases (Euclidean distances between spatial firing rate maps, control within versus across condition; see Extended Data Fig. 2a for test by decoding, median and CIs (bootstrap)). g, Spatial versus task phase information content of all neurons and position and state encoding for example cells. Gray, sum-normalized histograms (color scale as in d).