Signal-to-signal neural networks for improved spike estimation from calcium imaging data
Fig 3
Change in the evaluation measures with Gaussian windowing of the training targets.
(left) Bar diagram depicting the difference in Correlation, Rank and AUC when using the 100 Hz spike train as the target and when convolving this target with a Gaussian window to generate a smoothed training target. Two different windowing sizes are shown; 11 samples with 5 standard deviation and 33 samples with 11 standard deviation. (right) From top to bottom: An example calcium fluorescence signal and its corresponding training targets (spikes at 100 Hz, Gaussian targets with 5 and 11 samples each, and the ground truth at 10,000 Hz). Gaussian training targets are having an equivalent shape to the spikes target at 100 Hz. Note that all of them are approximations of the original ground truth.