Neuroptikon: a customizable tool for dynamic, multi-scale visualization of complex neural circuits
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1
HHMI, Janelia Farm Research Campus, United States
Groups around the world are using electron and light microscopy, optogenetics and physiology to map neural connectivity in a variety of brain regions and species. Having the circuit diagram for a brain region is potentially very useful, but how should such information be represented? The visualization of neural circuits becomes difficult as the density of both a circuit’s connections and the data associated with the components of the circuit increases. Visualizing the entire circuit causes fine detail to be lost, while focusing on specific components hides their context within the larger circuit. We have developed an open-source, Python-based, abstract circuit visualization package, Neuroptikon, which overcomes these problems with an expandable suite of interactive tools. Circuits are represented in Neuroptikon at both a biological and a visual level. A simple biological model allows the construction of neural circuits from regions, neurons and/or neurites and can be extended by user-defined attributes. A NetworkX (Hagberg et al. 2008) version of the circuit is maintained which allows graph-theoretic analysis. A flexible visualization layer sits above the model and allows any biological attributes (built-in or user-defined parameter-value pairs) to control the display of some or all of the circuit’s components. Circuit components can be positioned in two or three dimensions either manually or automatically by one of the included layout algorithms. Visualizations can be managed independently of circuits allowing multiple styles of visualization for the same circuit (say, an anatomically accurate layout of neurons versus a layout based on wiring optimization or one chosen for maximum clarity) and re-use of the same visualization for different circuits. Neuroptikon allows interaction with circuits through its graphical and scripting interfaces. A basic set of interactive tools including local connectivity highlighting and shortest path finding are provided by the graphical interface. Both the biological model and visualization layer can be queried and modified via the scripting interface. Script commands can be executed interactively or via saved scripts. The scripting interface also allows expansion of the user interface via new layout algorithms, custom inspectors, etc. We have developed a software package that enables an abstracted representation of neural circuits suitable for conceptual analysis and experimental design. This tool can also serve as a useful front-end for repositories of neuroanatomical and neurophysiological data, and citation databases indexed by neuroanatomical features. Neuroptikon has been used to model and visualize synapse level connectivity in C. elegans, compartmental arborizations in Drosophila and region level connectivity in the primate visual cortex, and we will present these test cases as part of a demonstration of the tool.
References
1. Aric A. Hagberg, Daniel A. Schult and Pieter J. Swart, "Exploring network structure, dynamics, and function using NetworkX", in Proceedings of the 7th Python in Science Conference (SciPy2008), Gäel Varoquaux, Travis Vaught, and Jarrod Millman (Eds), (Pasadena, CA USA), pp. 11-15, Aug 2008
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session II
Citation:
Midgley
F,
Olbris
DJ,
Chklovskii
D and
Jayaraman
V
(2010). Neuroptikon: a customizable tool for dynamic, multi-scale visualization of complex neural circuits.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00274
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Received:
05 Mar 2010;
Published Online:
05 Mar 2010.
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Correspondence:
Frank Midgley, HHMI, Janelia Farm Research Campus, Ashburn, United States, midgleyf@janelia.hhmi.org