Fig. 9.

By increasing the $r^2$ threshold used to match separated signals with underlying neural activity, we observe an increase in accuracy in spike detection based on threshold crossing for spike detection, but a decrease in the number of matched neuronal signals. By altering the spike detection threshold, we can generate a receiver operating characteristic curve measuring the accuracy of the spike detection algorithm. Using different $r^2$ thresholds to match separated signals with the simulated neural fluorescence traces, we can generate different ROC curves. By lowering the $r^2$ threshold, we match more traces ($N$), but sacrifice accuracy (measured as AUC). Average of five simulations based on 100 fibers with a splay of $\sigma =125\mu \mathrm{m}$ in a region with a cell density of 250,000 neurons per ${\mathrm{mm}}^3$.