Random forest models trained on heat kernel signature (HKS) features to predict the compartment target of a synapse on neuronal meshes. Method is described in more detail in Pedigo et al. bioRxiv 2026.
The feature generation pipeline used here can be found in the meshmash repo.
The parameters for that function are in parameters.toml.
synapse_hks_model.joblib takes in the 32 HKS features generated by that pipeline, as well as a column of the distance from each point to the postsynaptic nucleus.
The model outputs predictions for {"spine", "shaft", "soma"} targets. This is the model used in the paper.
simple_hks_model.joblib uses only the HKS features and outputs {"spine", "not_spine"} labels.
Please see the paper above for much more detail on the performance of the classifier and its application. Questions and comments are more than welcome: ben.pedigo@alleninstitute.org.