Extended Data Fig. 7: Correlations between bump profile and rotational speed.

Panels a-d show data from 28 flies walking in a virtual reality environment with a bright visual cue. Each variable was smoothed with a 10-s moving window, after first excluding time points where the fly was immobile or goodness of fit was below threshold. a. Example fly. Here and elsewhere in this figure, we used a 10-s rolling window to compute the rotational speed, bump width and bump amplitude values. b. Mean HD encoding accuracy (± s.e.m.) versus the fly’s rotational speed. As the fly’s rotational speed increases, HD encoding accuracy decreases (linear mixed-effects model, p < 0.001). c. Mean z-scored bump width (± s.e.m.) versus the fly’s rotational speed. As the fly’s rotational speed increases, so does bump width (linear mixed-effects model, p < 0.001). d. Mean z-scored bump amplitude (± s.e.m.) versus the fly’s rotational speed. As the fly’s rotational speed increases, so does the bump amplitude (linear mixed-effects model, p < 0.001). Panels e-g show data from 7 flies tested in separate experiments where we displayed a bright visual cue rotating around the fly. The cue rotated at a different speed for each block of the experiment. e. Mean cue position encoding accuracy (± s.e.m.) versus cue rotational speed. Cue position encoding accuracy is computed in the same way that we compute HD encoding accuracy, except that it measures encoding of cue position rather than HD. For example, a value of 1 would mean that the EPG bump perfectly tracks the cue, whereas a value of 0 would mean that the bump moves independently from the cue. Cue position encoding accuracy decreases as cue speed increases (linear mixed-effects model, p < 0.001). f. Mean bump width (± s.e.m.) versus cue rotational speed. Bump width increases as cue speed increases (linear mixed-effects model, p = 0.007). g. Mean bump amplitude (± s.e.m.) versus cue rotational speed. Bump amplitude decreases as cue speed increases (linear mixed-effects model, p < 0.001).