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ViSimpl involves two components: SimPart and StackViz. SimPart is a three-dimensional visualizer for spatio-temporal data that allow spatio/temporal analysis of the simulation data, using particle-based rendering. StackViz illustrates how the electrophysiological variables evolve over time and provides a temporal representation of the data at different aggregation levels. They allow users to visually discriminate the activity of different groups of neurons, and provide detailed information about individual neurons of interest. These components share synchroniszed playback control of the simulation being analyzsed and work together as linked views, although they are loosely coupled and can also be used independently. They are ready to be used with BlueConfig Datasets among other file formats such as specific HDF5 and CSV. VisSimpl can be coupled with NeuroScheme for adding functionality such as navigate through the underlying structure of the data using symbolic representations and different levels of abstraction.

Other software

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3D Slicer

3D Slicer is: A software platform for the analysis (including registration and interactive segmentation) and visualization (including volume rendering) of medical images and for research in image guided therapy. A free, open source software available on multiple operating systems: Linux, MacOSX and Windows Extensible, with powerful plug-in capabilities for adding algorithms and applications. Features include: Multi organ: from head to toe. Support for multi-modality imaging including, MRI, CT, US, nuclear medicine, and microscopy. Bidirectional interface for devices. There is no restriction on use, but Slicer is not approved for clinical use and intended for research. Permissions and compliance with applicable rules are the responsibility of the user.

Data analysis and visualisation


A MATLAB® toolbox that given a three-dimensional spine reconstruction computes a set of characteristic morphological measures that unequivocally determine the spine shape.

Modelling and simulation


Dendritic spines of pyramidal neurons are the targets of most excitatory synapses in the cerebral cortex and their morphology appears to be critical from the functional point of view. Thus, characterizing this morphology is necessary to link structural and functional spine data and thus interpret and make them more meaningful. We have used a large database of more than 7,000 individually 3D reconstructed dendritic spines from human cortical pyramidal neurons that is first transformed into a set of 54 quantitative features characterizing spine geometry mathematically. The resulting data set is grouped into spine clusters based on a probabilistic model with Gaussian finite mixtures. We uncover six groups of spines whose discriminative characteristics are identified with machine learning methods as a set of rules. The clustering model allows us to simulate accurate spines from human pyramidal neurons to suggest new hypotheses of the functional organization of these cells.

Data analysis and visualisationData

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