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. Author manuscript; available in PMC: 2018 Dec 1.

Published in final edited form as: Ann Appl Stat. 2018 Nov 13;12(4):2430–2456. doi: 10.1214/18-AOAS1159

Fig. 12. — We illustrate the performance of SCALPEL () and CNMF-E [Zhou et al. (2016)] () in terms of the average sensitivity (percent of true neurons detected; shown as a solid line) and precision (percent of neurons detected that are true neurons; shown as a dashed line) for the simulated calcium imaging data described in Section 7.1. For both, 95% confidence intervals are shown. In (a), we consider the performance for a fixed signal to independent noise ratio of 1.5 and varying signal to spatially correlated noise ratio. In (b), we consider the performance for a fixed signal to spatially correlated noise ratio of 1.5 and varying signal to independent noise ratio. Note that CNMF-E was unable to initialize neurons in the presence of a high amount of independent noise, so the CNMF-E results are omitted for ratios of 0.5 and 1 in (b).

Inline graphic — We illustrate the performance of SCALPEL () and CNMF-E [Zhou et al. (2016)] () in terms of the average sensitivity (percent of true neurons detected; shown as a solid line) and precision (percent of neurons detected that are true neurons; shown as a dashed line) for the simulated calcium imaging data described in Section 7.1. For both, 95% confidence intervals are shown. In (a), we consider the performance for a fixed signal to independent noise ratio of 1.5 and varying signal to spatially correlated noise ratio. In (b), we consider the performance for a fixed signal to spatially correlated noise ratio of 1.5 and varying signal to independent noise ratio. Note that CNMF-E was unable to initialize neurons in the presence of a high amount of independent noise, so the CNMF-E results are omitted for ratios of 0.5 and 1 in (b).