 doubled the anatomical data included in the dataset. Second, we have added data with a third molecular marker for the four F-RGCs (Rousso et al., 0000). Third, instead of clustering the data into separate groups, we have created a decision tree that assigns our anatomical data to the EM reconstructions available in the EyeWire museum (http://museum.eyewire.org/; Bae et al., 0000). We belief this data set provides an excellent quantitative description of almost the entire retinal ganglion cell population. By augmenting this database with data from identified retinal ganglion cells in other publications (F-Cells Rousso et al., 0000; Sumbul et al., 0000; HD0/HD0/vertical OS cells Jacoby and Schwartz 0000 and Nath and Schwartz, 0000) we were able to reliably assign each dendritic tree in our dataset to one of the cell-types in this database using a limited set of hierarchical decisions based on quantifiable characteristics of the neurons, starting with the stratification patterns of the neurons. This approach has allowed us to provide a rational for the total number of cell types that exist in our dataset and positively define their types. Fourth, we have added two statistical tests to determine:  a\) if a cell-type under or overrepresented in the two circuits (two-proportion z-test)  b\) if a cell-type is sampled significantly by a pathway (bootstrap test)  Finally, we have adjusted our language such that statements accurately reflect the data presented. We found that each cell-type that showed a statistically negative bias to one of the circuits was also found to not innervate that circuit with enough neurons to be statistically greater than 0. If these two tests are significant, then the cell type is considered to uniquely innervate one of the circuits and not the other (Figure 0). 