Neuronal morphologies provide the foundation for the electrical behavior of neurons, the connectomes they form, and the dynamical properties of the brain. Comprehensive neuron models are essential for defining cell types, discerning their functional roles, and investigating brain disease related dendritic alterations. However, a lack of understanding of the principles underlying neuron morphologies has hindered attempts to computationally synthesize morphologies for decades. We introduce a synthesis algorithm based on a topological descriptor of neurons, which enables the rapid digital reconstruction of entire brain regions from few reference cells. This topology-guided synthesis generates dendrites that are statistically similar to biological reconstructions in terms of morpho-electrical and connectivity properties and offers a significant opportunity to investigate the links between neuronal morphology and brain function across different spatio-temporal scales. Synthesized cortical networks based on structurally altered dendrites associated with diverse brain pathologies, revealed principles linking branching properties to the structure of large-scale networks. We provide here the original biological reconstructions, the artificially generated cells and related data (electrical traces, connectivity of artificial networks) that were used for the analysis of the paper "Computational synthesis of cortical dendritic morphologies" to appear in Cell Reports.

The morphological reconstructions used for this study are part of a publication that is currently in preparation; we would like to acknowledge the work of Ying Shi, Thomas Berger, Shruti Muralidhar, Rodrigo de Campos Perin and Zoltan Kisvarday for the collection of these reconstructions and we refer the reader to https://bbp.epfl.ch/sscx-portal/experimental-data/neuron-morphology/ for a proper citation of the morphological reconstructions.