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Study simulation data and their associated spatial and temporal features by exploratory visual analysis
Capitalise on the visual methods offered in ViSimpl to improve your ability to extract knowledge and thereby obtain a better understanding of brain simulation data. ViSimpl is a desktop application that offers the ability to navigate, analyse and interact with brain simulation data, using your choice of either spatial or temporal representations.
ViSimpl involves two components: SimPart and StackViz. SimPart provides spatio/temporal analysis of the simulation data, using particle-based rendering, while StackViz provides a temporal representation of the data at different aggregation levels. They allow a user to visually discriminate the activity of different groups of neurons, and provide detailed information about individual neurons of interest. These components share synchronised playback control of the simulation being analysed and work together as linked views, although they are loosely coupled and can also be used independently.
ViSimpl can be coupled with NeuroScheme for added functionality. The ViSimpl SimPart component provides a three-dimensional visualizer for spatiotemporal data, and the StackViz component provides a view of how the electrophysiological variables evolve over time. Adding NeuroScheme lets you navigate through the underlying structure of the data using symbolic representations and different levels of abstraction.
We are always happy to hear from users. Please visit our code repository to open an issue or leave us feedback, or contact us by sending an email to dev@vg-lab.es
Galindo, S.E., Toharia, P., Robles, Ó.D., Pastor, L. (2016) ViSimpl: Multi-View Visual Analysis of Brain Simulation Data. Front. Neuroinform. 10, 44. https://doi.org/10.3389/fninf.2016.00044
Galindo, S.E., Toharia, P., Robles, Ó.D., Ros, E., Pastor, L., Garrido, J.A. (2020) Simulation, visualization and analysis tools for pattern recognition assessment with spiking neuronal networks. Neurocomputing. 400, 309-321. https://doi.org/10.1016/j.neucom.2020.02.114