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Fig. 8

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ZDB-FIG-240514-50
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Varma et al., 2024 - A machine-learning tool to identify bistable states from calcium imaging data
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Fig. 8

CAMLSORT PREDICTIONS ON IMAGING DATA FROM ZEBRAFISH PNS A, raw CaMLsort predictions of cellular state inferred from experimental data acquired using widefield imaging (n = 15). Predictions from CaMLsort (middle) are compared against the ground truth state (top) at each time step. Each recording was treated independently, but predictions across cells have been stitched here for ease of visualization. Each recording is separated by a vertical dashed line, and the number above each prediction is the F1 score for that recording. The bottommost plot has posterior scores for every time step, with those for the ‘bursting’ class in blue, and those for the ‘tonic’ class in light orange. B, CaMLsort predictions (top) that remain after majority voting with a window length of seven steps has been used to retain only confident classifications (for details, see Methods and Results). The posterior score (bottom) for each class at each time step, taken as the average class posterior score across all seven-step windows that included said time step. As in (A), the posterior scores for the ‘bursting’ class are in blue, whereas those for the ‘tonic’ class are in light orange. C, the distribution of F1 scores between the ground truth state and various predictions from CaMLsort: raw predictions from the imaging data (far left), predictions obtained after majority voting (second from left), raw predictions obtained from calcium signals reconstructed from spike times (third from left) and those predictions from imaging ΔF/F traces that were scrambled prior to prediction. The notches indicate 95% confidence intervals around the median (red line).

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