Figure 6. Recurring movement motifs are sequenced diversely across mice and consistently across stimuli.

(A) Eight example frames from one instance of a behavioral motif with tracking overlaid. (B) Average motif shapes. Dots and lines show the average time course of posture for eight frames of each of the 11 motifs (n = 9 mice). All instances of each motif are translated and rotated so that the head is centered and the head-body axis is oriented upward in the first frame. Subsequent frames of each instance are translated and rotated the same as the first frame. Time is indicated by color (dark to light). Background color in each panel shows the color assigned to each motif. (C) Across-trial motif sequences for two behavioral sessions for one mouse. Trials are separated into trials where the mouse chose left and those in which the mouse chose right. Trials are sorted by duration. Both correct and incorrect trials are included. Color scheme as in (B). (D) Linear classifier analysis shows that mice can be identified from motif sequences on a trial-by-trial basis. Grayscale represents the fraction of trials from a given mouse (rows) that are decoded as belonging the data of a given mouse (columns). The diagonal cells represent the accuracy with which the decoded label matched the true label, while off-diagonal cells represent trials that were mislabeled by the classifier. Probabilities along rows sum to 1. Cells marked with asterisks indicate above chance performance (label permutation test, p<0.01). (E) Linear classifier analysis identifies odor omission trials above chance, but does not discriminate across odor concentration ratios (n = 9 mice). (F) Cross-validated log-likelihood (evaluated on trials not used for model fitting) for fit auto-regressive hidden Markov model (AR-HMM) models with different numbers of motifs, S, shows that model log-likelihood does not peak or plateau up to S = 100.

Figure 6—figure supplement 1. Motif statistics and examples and linear decoder results for 80:20 experiments.

(A) Example nose trace across a single trial colored by motif identity. (B) Linear decoder (Figure 6; Materials and methods: linear decoding section) results for Variable |C| experiments (n = 5). (C) Fraction of motif usage across all mice (n = 8) for the model with S = 16. Black points are individual mice, black line is average across mice, and shaded region is ±1 standard deviation. Colors on x-axis represent motifs used in analysis (Figure 6) and y-axis are fractions of frames that motif occupies. (D) The average dwell time of each motif across all mice (n = 8) for the model with S = 16. Black points are individual mice, black line is average across mice, and shaded region is ±1 standard deviation. Colors on x-axis represent motifs used in analysis (Figure 6) and y-axis are fractions of frames that motif occupies.

Figure 6—figure supplement 2. Motif shapes, sequences, transition matrices, and sniff synchronization for an auto-regressive hidden Markov model capped at a maximum of six states.

(A-E) As in Figure 6.

Figure 6—figure supplement 3. Motif shapes, sequences, transition matrices, and sniff synchronization for an auto-regressive hidden Markov model capped at a maximum of 10 states.

(A-E) As in Figure 6.

Figure 6—figure supplement 4. Motif shapes, sequences, transition matrices, and sniff synchronization for an auto-regressive hidden Markov model capped at a maximum of 20 states.

(A-E) As in Figure 6.

Figure 6—figure supplement 5. Motif shapes across individuals.

Average of first eight frames of the 11 commonly used motifs across individuals. Dots and lines show the average time course of posture for eight frames of each of the 11 motifs. All instances of each motif are translated and rotated so that the head is centered and the head-body axis is oriented upward in the first frame. Subsequent frames of each instance are translated and rotated the same as the first frame. Time is indicated by color (dark to light). Each color/column represents a single motif and each row an individual mouse.