This project measures statistical properties from sets of digital reconstructions of dendritic morphologies. It then stochastically generates new morphologies based on the measured statistical distributions. The key innovation of this tool's approach is to estimate the change in branching rate as a function of distance from soma by modeling branching rate as a heterogeneous Poisson process. A point process filter is used to estimate the branching rate. The uploaded example includes a data set for measuring the the morphology of dentate granule cells and generating new granule cells.
Chou, Z. Z., Yu, G. J., & Berger, T. W. (2020). Generation of Granule Cell Dendritic Morphologies by Estimating the Spatial Heterogeneity of Dendritic Branching. Frontiers in Computational Neuroscience, 14. https://doi.org/10.3389/fncom.2020.00023