Fig. 5: Hierarchical predictions enable dataset-wide circuit analyses.

a, Diagram of the hierarchical model framework used to predict neuronal and non-neuronal subclasses using a set of five classifiers. Nucleus and soma features alone were used for models 1–4. PSS features were added to predict inhibitory subclasses in model 5 (Extended Data Table 1). Oligo, oligodendrocyte. b, Confusion matrix of the cross-validation performance for all cells within the manually labelled column. Note that classifiers for excitatory neurons, inhibitory neurons and non-neurons were trained separately (models 2, 4 and 5 in a). The confusion rate between these classes can be seen in Extended Data Fig. 4. c, 2D UMAP embedding inferred from depth, nucleus and soma features of all cells in the dataset coloured by the hierarchical model predictions (n = 94,010). d, Left: 2D rendering of a representative 23P cell morphology, with dendrite in black and axon in grey. Points represent the somatic position of all downstream target cells coloured by the hierarchical model subclass prediction. Right: synapse count (top), total synapse area (middle; voxels are 4 × 4 × 40 nm) and number of synapses per connection (bottom) displayed by the model-predicted subclasses illustrating the local targeting profile of this individual cell. e, Similar information as in d but for an inhibitory bipolar cell that is predicted to preferentially target basket cells. This unique population of bipolar cells has been further characterized¹⁵. For all box plots: centre line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; outliers shown.