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The definition of the soma is fuzzy, as there is no clear line demarcating the soma of the labeled neurons and the origin of the dendrites and axon. Thus, the morphometric analysis of the neuronal soma is highly subjective. This software provides a mathematical definition and an automatic segmentation method to delimit the neuronal soma. We applied this method to the characterization of pyramidal cells, which are the most abundant neurons in the cerebral cortex. Thus, this software is a means of characterizing pyramidal neurons in order to objectively compare the morphometry of the somata of these neurons in different cortical areas and species.


Luengo-Sanchez, S., C. Bielza, R. Benavides-Piccione, I. Fernaud-Espinosa, J. DeFelipe, and P. Larrañaga, “A univocal definition of the neuronal soma morphology using Gaussian mixture models”, Frontiers in Neuroanatomy, vol. 9, issue 137, 2015

Optimal Wiring

The way in which a neuronal tree expands plays an important role in its functional and computational characteristics. This software enables the user to analyze the wiring optimality of a three-dimensional neuron from its specification in .asc format. The software and a user manual are available for download by clicking the link below. It takes the branching points of the real three-dimensional neuronal reconstruction and it searches for the minimal wiring arborization structure that respects the branching points. The software is capable of processing wiring design problems with point clouds up to size 200. Both dendritic and axonal wiring can be analyzed.

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Anton-Sanchez, L., C. Bielza, R. Benavides-Piccione, J. DeFelipe, and P. Larrañaga, “Dendritic and axonal wiring optimization of cortical GABAergic interneurons”, Neuroinformatics, vol. 14, issue 4, pp. 453-464, 2016

Reconstruction files of pyramidal cells

By clicking the link below the user can download the specification in .asc format of the 288 pyramidal cells analyzed in [3]. The software for analyzing the wiring optimality is freely available above

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Anton-Sanchez, L., C. Bielza, P. Larrañaga, and J. DeFelipe, “Wiring Economy of Pyramidal Cells in the Juvenile Rat Somatosensory Cortex”, PLoS ONE, vol. 11, issue 11, 2016.


Voltric is an open source software library for clustering with Bayesian networks that is being implemented by researchers of the Polythecnic University of Madrid. While still on early development, it aspires at becoming an important tool in the Data science ecosystem. Voltric provides an extensive framework for Probabilistic Graphical Models with a special focus on Bayesian networks. It equips the user with a set of state-of-the-art techniques and provide the basis for the creation of new ones.


3DSpineMFE and spineSimulation 

The dendritic spines of pyramidal neurons are the targets of most excitatory synapses in the cerebral cortex. They have a wide variety of morphologies, and their morphology appears to be critical from the functional point of view. 3DSpineMFE and spineSimulation are two software tools that allow to characterize dendritic spine geometry, perform model-based clustering according to dendritic spine morphology and simulate artificial dendritic spines. In combination, these software are a useful tool for theoretical predictions on the functional features of human pyramidal neurons based on the morphology of dendritic spines.

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Download spineSimulation

Luengo-Sanchez, S., I. Fernaud-Espinosa, C. Bielza, R. Benavides-Piccione, P. Larrañaga, and J. DeFelipe, “3D morphology-based clustering and simulation of human pyramidal cell dendritic spines”, PLOS Computational Biology, 2018.