Article Figures & Data
Figures
- Figure 1.
Behavior at scale. A, top panel, Five consecutive frames from an individual well of a 96-well plate as a 6 dpf zebrafish larva performs a swim bout. Blue highlights pixels that change intensity between frames (Δ pixels). Lower panel. A Δ pixels time series from the larva above. Highlighted are the features that describe each active and inactive bout. B, Mean bout frequency (Hz) recorded from individual larvae at 5 and 6 dpf during the day (light blue) and the night (dark blue). Each dot is 1 of 124 wild-type larvae. The orange crosses mark the population means. C, The probability of observing different lengths of inactivity during the day (light blue) or the night (dark blue) at 5 and 6 dpf. Each larva’s data were fit by a pdf. Shown is a mean pdf (bold line) and SD (shaded surround) with a log scale on the x-axis cropped to 10 s. Inset, The total probability of inactive bout lengths longer than 10 s, per animal. D, The mean activity of 124 wild-type larvae from 4 to 7 dpf, on a 14/10 h light/dark cycle. Data for each larva was summed into seconds and then smoothed with a 15-min running average. Shown is a summed and smoothed mean Δ pixels trace (bold line) and SEM (shaded surround). E, Average activity across one day (white background) and night (dark background) for larvae dosed with either DMSO (control) or a range of melatonin doses immediately before tracking at 6 dpf. Data were summed and smoothed as in D. The number of animals per condition is denoted as n. Extended Data Figures 1-1, 1-2, 1-3 support Figure 1.
- Figure 5.
Pharmacological behavioral motifs. A, left, Module sequences for the single best motif for each melatonin comparison. Modules are colored as elsewhere. Middle: for each dose’s single best motif, see left panel y-axis for dose, enrichment/constraint scores are shown for every dose on a log x-axis. Each animal is shown as a dot, with a mean ± std overlaid per dose. Right, A two-dimensional tSNE embedding from a space of 912 unique motifs. Each animal is shown as a single dot underlaid by a shaded boundary encompassing all animals in each condition. B, left, Module sequences for the single best motif for each PTZ comparison. To highlight a seizure specific motif, the control motif and corresponding enrichment/constraint score shown is mRMR motif 2, not 1, for this comparison. Modules are colored as elsewhere. Middle, For each dose’s single best motif, enrichment/constraint scores are shown for every dose on a linear x-axis. Each animal is shown as a dot, with a mean and SD overlaid per dose. Right, A two-dimensional tSNE embedding from a space of 338 unique motifs. Each animal is shown as a single dot underlaid by a shaded boundary encompassing all animals in each condition. C, Each classifier’s classification error (%) is shown in terms of modules (x-axis) and motifs (y-axis). Data are shown as mean and SD from 10-fold cross validation. Classifiers are colored by experimental dataset (see Legend). For reference, y = x is shown as a broken black line. Data below this line demonstrates superior performance of the motif classifiers.
- Figure 2.
Unsupervised learning identifies contextual behavioral modules. A, Average Δ pixels changes for each active module. Shown is the mean (bold line) and SEM (shaded surround) of 100 bouts randomly sampled from each module from one representative larva. Modules are numbered and colored by average module length across all animals, from shortest (1) to longest (5). B, A probability density curve showing the distribution of inactive bout lengths in seconds, on a log x-axis cropped to 60 s. Modules are numbered and colored from shortest (1) to longest (5) mean length (see legend for each module’s minimum and maximum bout length). C, Matrices showing the active (left) or inactive (right) module assignment of every frame (x-axis) for each of 124 wild-type larvae (y-axis) across the 14-h days (light blue underlines) and 10-h nights (dark blue underlines) from 5 to 6 dpf. Larvae were sorted by total number of active modules from highest (top) to lowest (bottom). Modules are colored according to the adjacent colormaps. D, Average active (upper) and inactive (lower) module probability during day (light blue) and night (dark blue) 5 and 6 of development. Each of 124 wild-type animals is shown as a dot and orange crosses mark the population means. Active modules are sorted by mean day probability from highest to lowest (left to right). Inactive modules are sorted by mean length from shortest to longest (left to right). The blobs correspond to the color used for each module in other figures. E, The mean frequency of each active (left) and inactive (right) module across days 5 and 6 of development. Shown is a mean smoothed with a 15-min running average, rescaled to 0–1. Days are shown with a white background, nights with a dark background. Modules are sorted from shortest to longest (lower to upper panels). Extended Data Figures 2-1, 2-2 support Figure 2.
- Figure 3.
Hierarchical compression reveals structure in zebrafish behavior. A, Compression explained using fictive data. Top to bottom, From Δ pixels data (black trace), we classified both active and inactive behaviors into modules (colored circles). From modular behavioral sequences, we identified motifs (sequences of modules) using a compression algorithm. Compression iteratively identifies motifs (shown as boxes) by replacing them with new symbols until no more motifs can be identified and the sequence is maximally compressed. B, Each panel shows how compressibility, calculated from 500 module blocks, varies in different behavioral contexts. Each pale line shows an individual fish’s mean compressibility during the day and the night. The darker overlay shows a population day and night mean ± SD. In the wild-type data, compressibility is higher during the day than the night (p < 10−158) and increases from day/night 5–6 (p < 10−4), findings consistent across triplicate experiments. Melatonin decreases (p < 10−10), while PTZ increases compressibility (p < 10−8). There is no effect of hcrtr genotype on compressibility. Statistics are two-way or four-way ANOVA. C, All 46,554 unique motifs (y-axis) identified by compressing data from all animals. Each motif’s module sequence is shown, with the modules colored according to the colormap on the right. Motifs are sorted by length and then sequentially by module. Motifs range in length from 2 to 20 modules long. Inset, For each motif length, the probability of observing each inactive or active module. D, Each motif in the library consists of an alternating sequence of Δ pixels changes and pauses (active and inactive modules). A representative motif of each module length is shown with each module colored according to the colormap in C. Representative motifs were chosen by determining every motif’s distribution of modules and then for each observed module length, selecting the motif closest to the average module distribution (see C, inset). Extended Data Figure 3-1 supports Figure 3.
- Figure 4.
Supervised learning identifies contextual behavioral motifs. A, pdfs showing the probability of observing motifs at different enrichment/constraint scores rounded to whole numbers and summed at values above or below ±4 for ease of visualization. Each wild-type animal is depicted by a single pale blue (real data) and 10 black (shuffled data) lines; overlaid in bold are mean pdfs. The inset shows that the kurtosis of the real data are higher than the shuffled data (p < 10−271; two-way ANOVA, real vs shuffled data, no significant interaction with experimental repeat factor). Each larva is shown as a pale line; overlaid is a population mean and SD. B, Enrichment/constraint scores for all 46,554 motifs (x-axis) for each fish during day/night 5 and 6 of development (y-axis). To emphasize structure, motifs are sorted in both axes, first by their average day/night difference (from day to night enriched left to right), then separately day and night by larva. Finally, each motif’s enrichment/constraint score is Z-scored to aid visualization. C, left, The 15 day/night mRMR motifs module sequences are shown numbered by the order in which they were selected by the algorithm. Motifs are sorted by day minus night enrichment/constraint score (middle). The long pauses at the end of motifs 5 and 14 are cropped at 10 s (arrows). Middle, For each selected motif (y-axis), ordered as in the left panel, each wild-type animal’s (124 in total) day minus night enrichment/constraint score (x-axis) is shown as a dot. Values above zero are colored light blue; below zero are dark blue. Overlaid is a population mean and SD per motif. Right, A tSNE embedding of the 15-dimensional motif data (middle) into a two-dimensional space. Each circle represents a single day (light blue) or night (dark blue) sample. D, Representative motif temporal dynamics; shown are motifs 1 (day) and 2 (night) from C, as well as a startle-like motif. Left, Each motif’s module sequence. Right, Each motif’s mean enrichment/constraint score each hour, rescaled to 0–1. Extended Data Figure 4-1 supports Figure 4.
Tables
Comparison Motifs (number) Cv error (%) Cv error Std Mc error (%) Mc EP RM error (%) RM error Std Wild type Day/night 15 0.20 0.63 50.0 2.25 9.25 2.50 Day 5/day 6 93 20.2 9.60 50.0 3.18 45.8 4.09 Night 5/night 6 85 19.8 8.09 50.0 3.18 48.3 6.00 Day hours •09-10 102 6.39 1.23 7.14 0.44 7.67 0.26 •10-11 1 7.37 0.31 7.14 0.44 7.18 0.10 •11-12 5 7.20 0.23 7.14 0.44 7.15 0.03 •12-13 9 7.06 0.34 7.14 0.44 7.19 0.08 •13-14 1 7.14 0.12 7.14 0.44 7.14 0 •14-15 1 7.14 0.12 7.14 0.44 7.14 0 •15-16 1 7.11 0.14 7.14 0.44 7.14 0 •16-17 1 7.09 0.15 7.14 0.44 7.14 0 •17-18 1 7.14 0.12 7.14 0.44 7.14 0 •18-19 1 7.14 0.12 7.14 0.44 7.14 0 •19-20 1 7.14 0.12 7.14 0.44 7.14 0 •20-21 3 7.11 0.27 7.14 0.44 7.15 0.01 •21-22 1 7.14 0.12 7.14 0.44 7.14 0 •22-23 1 7.14 0.12 7.14 0.44 7.15 0.03 Night hours •23-24 23 0.69 0.47 10.0 0.60 6.57 1.23 •24–01 177 9.84 1.83 10.0 0.60 11.6 0.78 •01–02 5 9.92 0.51 10.0 0.60 10.0 0.05 •02-03 88 9.72 1.18 10.0 0.60 10.4 0.31 •03-04 1 10.0 0.17 10.0 0.60 10.0 0.04 •04-05 22 9.92 0.47 10.0 0.60 10.1 0.07 •05-06 1 10.0 0.17 10.0 0.60 10.0 0 •06-07 1 10.0 0.17 10.0 0.60 10.0 0.02 •07-08 3 9.84 0.34 10.0 0.60 10.0 0.01 •08-09 1 10.0 0.17 10.0 0.60 10.0 0.01 Morning/evening 229 33.2 2.32 50.0 0.85 44.7 0.96 Early/late night 26 36.4 2.18 50.0 1.00 43.4 1.55 A table showing the performance of each wild-type motif classifier. Each classifier sought to separate the data shown in the comparison column, e.g., day/night. For the hourly comparisons, each hour was compared with data from all other hours grouped together. For each comparison, 250 motifs were chosen by mRMR, then a smaller number were retained (see motifs column) based on classification error curves (Extended Data Fig. 4-1A). Cv, 10-fold cross validated; Std, SD across the 10 folds; Mc, majority class classifier; EP, SE of proportion; RM, classifiers built from random motif subsets.
Comparison Motifs (number) Cv error (%) Cv error Std Mc error (%) Mc EP RM error (%) RM error Std hcrtr Day and night •WT/Het 173 25.5 6.77 27.7 1.88 39.0 1.93 •WT/Hom 83 24.7 6.07 50.0 2.83 47.8 3.66 •Het/Hom 235 24.7 3.76 27.7 1.88 38.6 1.83 Day •WT/Het 80 19.5 9.60 27.7 2.66 37.9 1.08 •WT/Hom 195 16.7 7.50 50.0 4.00 48.7 4.22 •Het/Hom 55 22.7 7.02 27.7 2.66 33.8 2.01 Night •WT/Het 79 16.3 6.38 27.7 2.66 37.1 7.17 •WT/Hom 53 12.8 9.58 50.0 4.00 52.3 6.16 •Het/Hom 76 16.0 7.27 27.7 2.66 36.0 5.05 Melatonin (day) •Control 40 0 0 25.0 4.42 16.4 3.67 •0.01μM 89 1.39 4.52 16.7 4.39 30.0 15.2 •0.1μM 192 1.39 4.52 16.7 4.39 20.5 18.0 •1μM 132 2.78 6.02 16.7 4.39 29.5 8.67 •3μM 97 0 0 16.7 4.39 48.6 11.2 •10μM 250 2.78 6.02 16.7 4.39 20.0 9.40 •30μM 133 0 0 16.7 4.39 32.2 7.89 PTZ (day) •Control 26 0 0 46.2 6.91 15.8 6.43 •2.5 mM 55 0 0 35.7 9.06 42.1 11.4 •5 mM 162 0 0 32.1 8.83 34.3 18.0 •7.5 mM 104 0 0 32.1 8.83 49.9 14.7 A table showing the performance of each classifier. Each classifier sought to separate the data shown in the comparison column, e.g., hcrtr+/+ (WT) and hcrtr−/+ (Het). For the pharmacological comparisons, each condition was compared with the rest of the conditions grouped together, aside from the control data which was excluded. For each comparison, 250 motifs were chosen by mRMR, then a smaller number were retained (see motifs column) based on classification error curves (see Extended Data Fig. 4-1A). Cv, 10-fold cross validated; Std, SD across the 10 folds; Mc, majority class classifier; EP, SE of proportion; RM, classifiers built from random motif subsets; WT, hcrtr+/+; Het, hcrtr−/+; Hom, hcrtr−/−.
Comparison Modules (number) Cv error (%) Cv error Std Mc error (%) Mc EP Wild type •Day/Night 10 1.61 1.29 50.0 2.25 •Day 5/Day 6 8 21.0 6.53 50.0 3.18 •Night 5/Night 6 1 35.5 9.71 50.0 3.18 hcrtr Day and night •WT/Het 1 27.7 0.77 27.7 1.88 •WT/Hom 10 45.8 10.9 50.0 2.83 •Het/Hom 8 27.5 1.12 27.7 1.88 Day •WT/Het 1 27.7 1.46 27.7 2.66 •WT/Hom 1 40.4 12.5 50.0 4.00 •Het/Hom 3 27.3 2.35 27.7 2.66 Night •WT/Het 1 27.7 1.46 27.7 2.66 •WT/Hom 1 47.4 10.9 50.0 4.00 •Het/Hom 10 27.0 1.72 27.7 2.66 Melatonin (day) •Control 3 8.33 8.69 25.0 4.42 •0.01μM 10 2.78 6.02 16.7 4.39 •0.1μM 2 16.7 4.52 16.7 4.39 •1μM 1 18.1 7.74 16.7 4.39 •3μM 1 16.8 8.67 16.7 4.39 •10μM 1 16.8 4.52 16.7 4.39 •30μM 1 16.8 4.52 16.7 4.39 PTZ (day) •Control 1 1.92 5.27 46.2 6.91 •2.5 mM 1 17.9 17.6 35.7 9.06 •5 mM 1 28.6 22.3 32.1 8.83 •7.5 mM 10 20.0 26.1 32.1 8.83 A table showing the performance of each module classifier. Each classifier sought to separate the data shown in the comparison column, e.g., wild type, day/night. For each comparison, all 10 modules were sequentially chosen by the mRMR algorithm, then a smaller subset was retained (see module column) based on classification error curves. Cv, 10-fold cross validated; Std, SD across the 10 folds; Mc, majority class classifier; EP, SE of proportion.
Movies
- Movie 1.
High-throughput behavioral tracking. A video of 96, 6 dpf zebrafish larvae swimming in our rig. The last 1 s of each larva’s Δ pixels data is plotted over each well. This video was filmed at 25 Hz and is played back in real time.
- Movie 2.
Behavioral modules. A video of 96, 6 dpf zebrafish larvae swimming in our rig. The last 1 s of each larva’s Δ pixels data is plotted over each well, with each active and inactive bout colored according to its module assignment. This video was filmed at 25 Hz and is played back in real time.
Extended Data 1
Supplementary Paper Code_Extended Data. Download Extended Data 1, ZIP file.
Extended Data 2
Supplementary Legion Code_Extended Data. Download Extended Data 2, ZIP file.
Extended Data Figure 1-1
Behavioral set-up and analysis. A, Schematic of our behavioral set-up. Note that aside from the computer, the set-up is fully enclosed. Not shown to scale. IR, infrared; LED, light emitting diode. B, Normalized temporal correlation of active bout starts between 24 wild-type larva (6 dpf) across 24 h. Pairwise correlations were computed and then grouped into three groups: autocorrelation (orange); neighbor fish, defined as larva in adjacent wells, diagonals excluded (light blue); and distant, non-neighbor fish (dark blue). Data from each group are plotted as a mean (bold line) and SD (shaded surround). Note that the y-axis is cropped from 1, where autocorrelation peaks, to 0.1. C, A fictive illustration of zebrafish behavior (blue line). Two minutes of data are shown divided by a black dashed vertical line. A 1-min binning approach would score both minutes as 20 s of activity and miss the 60-s period of inactivity in between. This latter loss leads to a discrepancy in the number of periods ≥60 s between the 1-min bin and 25-Hz methods (see D). D, The number of inactive periods ≥60 s for each of 124 wild-type animals is shown, as determined by both a 1-min bin and 25-Hz approach. Data are from each animal’s entire recording period (4–7 dpf). Data for each animal is shown as a pale blue line overlaid with a bold line showing the population mean and SD. Inset, The percentage of the 25-Hz counts detected by the 1-min bin method per animal. Each animal’s data are shown by a circle. An orange cross marks the population mean. E, Average activity across one day (white background) and night (dark background) for larvae exposed to either H2O (control) or a range of PTZ doses immediately prior to tracking at 6 dpf. Data for each larva was summed into seconds and then smoothed with a 15-min running average. Shown is a mean summed and smoothed trace (bold line) and SEM (shaded surround); n denotes the number of animals per condition. Download Figure 1-1, TIF file.
Extended Data Figure 1-2
Bout Features. A, Bout feature distributions during the day (light blue) and the night (dark blue). For the probability curves, each animal’s data were fit with a pdf. Shown is a mean pdf (bold line) and SD (shaded surround) with a log scale on the x-axis. For the scatter plots, each larva’s mean value across the days or nights (5–6 dpf) is shown as a light blue (day) or dark blue circle (night). An orange cross marks each population’s mean. Of the pdfs, only the mean day and night active bout total and inactive bout length pdfs were consistently significantly different across three independent experiments (p < 0.01; two-sample Kolmogorov–Smirnov test); n = 124 wild-type larvae. B, Melatonin bout feature means. A mean was taken per animal per feature, and day or night (6 dpf). Shown is a population mean and SEM during the day (white background) and the night (grey background). Control, DMSO; n = 24 controls then n = 12 per dose. C, PTZ bout feature means, as in B. Control, H2O; n = 24 controls then n = 10 (2.5 mM), n = 9 (5 mM), and n = 9 (7.5 mM). D, hcrtr bout feature means as in B, for days (white background) and nights (grey background) 5–6 post fertilization. hcrtr-/- mutants had significantly lower mean values compared to both hcrtr+/+ and hcrtr-/+ for the following active bout features: length, SD and total (p < 0.05 for all comparisons, Dunn–Sidak corrected four-way ANOVA, adjusted for the following factors: day/night, development, and experimental repeat). No features differed significantly between hcrtr-/+ and hcrtr+/+; n = 39, 102, and 39; for WT, hcrtr+/+; Het, hcrtr-/+; and Hom, hcrtr-/-, respectively. Download Figure 1-2, TIF file.
Extended Data Figure 1-3
Analysis framework. Flow diagram depicting the steps of our analysis framework. Data are output from our behavioral set-up (ViewPoint) in the form of a .xls file. perl_batch_192.m organizes these data to a .txt format. Experiment metadata (e.g., animal genotypes) are supplied in the form of a .txt file. The 1-min bin method uses sleep_analysis2.m to produce figures and statistics from these two .txt files. The 25-Hz method exports .raw data from ViewPoint to produce .xls files. Vp_Extract.m reorganizes these, using .txt data, to a .mat file which can be input to either Vp_Analyse.m or Bout_Clustering.m. Vp_Analyse.m produces figures and statistics. Bout_Clustering.m uses the clustering function gmm_sample_ea.m to assign data to modules, produce figures, and calculate statistics, Bout_Clustering.m’s output can be input to Bout_Transitions.m, which compresses full modular sequences by calling Batch_Compress.m and Batch_Grammar_Freq.m. The motifs identified from this approach can be input to Batch_Transitions_Hours.m which compresses 500 module chunks and uses Batch_Grammar_Freq.m to count motif occurrences per hour. With the exception of the 1-min bin method (sleep_analysis2.m), two example figures are shown for each figure producing step. All code can be run locally, although for speed several steps (indicated in green) are best run on a cluster computer. Download Figure 1-3, TIF file.
Extended Data Figure 2-1
Evidence accumulation-based clustering. A, left, Scree plot showing the percentage of variance explained by each principal component from the active bout data. The first three principal components, the knee point of the curve, were kept for subsequent analysis. The colors of these points refer to the right panel. Right, Each of the three retained component’s coefficients for the different active bout parameters is shown. B, The active bouts within each module were fit by Gaussian distributions. Each active bout is shown in a 3D space of PC1, PC2, and probability. Each bout is numbered and colored by its module assignment. C, Evidence accumulation (E.A.) matrix for the 40,000 active probe points (matrix dimensions are thus 40,000 by 40,000). A higher E.A. index indicates a higher frequency of pairwise occurrences in the same cluster across 200 Gaussian mixture models. This matrix was clustered hierarchically, and a maximum lifetime cut was made to determine the final number of modules. The dendrogram above shows all 40,000 leaves and is colored by mean module length from shortest (lightest) to longest (darkest) as in other figures. 500. Evidence accumulation matrix for the inactive bouts. Download Figure 2-1, TIF file.
Extended Data Figure 2-2
Behavioral modules. A, pdfs for each bout feature by module. All features are shown on a log x-axis. The legend panel indicates each module’s color. B, Melatonin module probabilities during 6 dpf day (upper panels) and night (lower panels) for both the active (left) and inactive (right) modules. Shown is a mean and SEM for each group, colored according to the legend. Active modules are sorted from highest to lowest by average wild-type day probability, based upon wild-type data in Figure 2D. Inactive modules are sorted by increasing mean length. Control, DMSO; n = 24 controls then n = 12 per dose. C, PTZ data as in B, with H2O (control); n = 24 controls then n = 10 (2.5 mM), n = 9 (5 mM), and n = 9 (7.5 mM). D, hcrtr data as in B, with mean values across 5 and 6 dpf. No module probabilities differed significantly among genotypes (full four-way ANOVA, with the following factors: genotype, day/night, development, and experimental repeat); n = 39, 102, and 39; for WT, hcrtr+/+; Het, hcrtr-/+; and Hom, hcrtr-/-, respectively. Download Figure 2-2, TIF file.
Extended Data Figure 3-1
Hierarchical compression metrics. A, The compressibility (y-axis) of the real wild-type data is higher than the paired shuffled data (p < 10−15, two-way ANOVA, real vs shuffled data, no significant interaction with experimental repeat factor). Each animal’s data are shown as a pale blue line. Overlaid is a mean and SD. Inset, The mean difference in compressibility between each larva’s real and shuffled data. Each larva is shown by a circle, and the orange cross marks the mean. B, The compressibility (y-axis) of the real wild-type data varies non-linearly with uncompressed sequence length. Each larva (of 124) is shown as a dot. C, The number of motifs (y-axis) identified from compressing each wild-type animal’s real and paired shuffled data. Each animal’s data are shown as a pale blue line. Overlaid is a mean and SD. Inset, The mean intra-fish difference in the number of identified motifs. Each larva is shown by a circle, and the orange cross marks the mean. D, Motif length (x-axis) and usage probability (y-axis) across the entire real (blue) and 10 shuffled datasets (black). Note that each shuffled dataset is plotted independently. For each motif length a grey line joins the real and mean shuffled value. E, Each panel shows how Δ compressibility varies in different behavioral contexts. Each pale line shows an individual larva’s average Δ compressibility during the day and the night. The darker overlay shows a population day and night mean and SD. F, Δ Compressibility of 500 module blocks for each wild-type larva, averaged into 1-h time points. Each pale blue line shows 1 of 124 larvae. Line breaks occur when a larva had <500 modules within a given hour. The darker blue overlay shows the mean and SD of these data every hour. Shown are days (white background) and nights (dark background) 5 and 6 of development. Download Figure 3-1, TIF file.
Extended Data Figure 4-1
Motif classifier performance. A, Classification error (%) from linear classifiers separating wild-type day and night behavior using motif enrichment/constraint scores as sequential mRMR motifs from 1 to 250 are added (x-axis). The average error is shown in light blue. Overlaid in darker blue is a running average three motifs wide. The broken black lines show the minimum of the smoothed data to be at 15 motifs, where the classification error is 0.2%. B, Wild-type temporal classifier performance. Real classifiers (color) are shown as a mean and SD from 10-fold cross validation. Majority class classifiers (grey) are shown as value and SE of proportion. Each classifier’s data are listed on the x-axis. D, day; N, night; M/E, morning/evening; E/LN, early/late night. The number of motifs chosen for each classification and exact values for each classifier are detailed in Table 1. C, hcrtr, Melatonin and PTZ classifier performance. Real classifiers (color) are shown as a mean and SD from 10-fold cross validation. Majority class classifiers (grey) are shown as value and SE of proportion. Each classifier’s data are listed on the x-axis. For hcrtr comparisons, grouped classifiers as well as separate day (light blue underline) and night (dark blue underline) classifiers are shown. For melatonin and PTZ, only day data were compared. Classifier details can be found in Table 2. Download Figure 4-1, TIF file.
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