Extended Data Fig. 7: In addition to explicitly encoding number of visited landmarks, RSC and the ANN exhibit higher trial-to-trial variability in partial information states.

(a) Bottom: Mean spatial activity profile of 2 example ANN neurons for LM1 and LM2. Average tuning is higher for the LM2 state. Top: same data as histograms, showing that the less well-tuned LM1 state corresponds to a bimodal rate distribution (rates are high in some trials, low in others) that transitions to a unimodal distribution once the 2^nd landmark has been identified in LM2. Data are from experiment configuration 2 (See Methods, section ‘Overview over experiment configurations used with ANNs’). Tuning curves were calculated using 20 bins of location/displacements and normalized individually for each neuron. The first time step in each trial and time steps with non-zero landmark input were excluded from the analysis. For histograms, each condition was binned in 100 column bins and neuron rates in 10 row bins. Histograms were normalized to equal sum per column. (b) Similarly, RSC rates are more dispersed per location in LM1. Schematic of analysis: firing rates were low pass filtered at 0.5 Hz, and for each location, the distribution of rates was computed in 8 bins, between the lowest and highest rate of that cell. (c) Example analysis for one cell. Top: Rate distribution resolved by 2D-location (4×4 bins) for example RSC neuron. Bottom: the resulting 16 histograms for LM1 and LM2 each, red dotted example histograms correspond to indicated example location (red dotted circles). (d) Summary statistics showing a more dispersed rate distribution per location in LM1. In sum, this analysis shows that in addition to the explicit encoding of uncertainty by a stable rate code (conjunctive with position and other variables), as shown in Fig. 1d,e,f and Extended Data Fig. 2a, where one would not expect a higher degree of trial-to-trial variability with higher uncertainty, there is still a degree of increased variability in states where the mouse might ‘take a guess’ that would differ between trials. This parallels a similar behavior in the ANN (panel a).