Network Analysis and Visualization of Mouse Retina Connectivity Data

The largest available cellular level connectivity map, of a 0.1 mm sample of the mouse retina Inner Plexiform Layer, was analysed using network models and visualized using spectral graph layouts and observed cell coordinates. This allows key nodes in the network to be identified with retinal neurons...

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Bibliographic Details
Published in:PloS one 2016-07, Vol.11 (7), p.e0158626
Main Author: Pailthorpe, Bernard A
Format: Article
Language:English
Subjects:
Algorithms
Amacrine cells
Amacrine Cells - physiology
Animal models
Animals
Biology and Life Sciences
Bipolar cells
Brain
Circuits
Codes
Collaboration
Computer and Information Sciences
Ganglia
High flow
Image Processing, Computer-Assisted
Linear algebra
Links
Medicine and Health Sciences
Mice
Microscopy
Microscopy, Electron
Nerve Net - physiology
Nervous system
Network analysis
Neural circuitry
Neural networks
Neurons
Nodes
Observations
Physiological aspects
Retina
Retinal Bipolar Cells - physiology
Retinal ganglion cells
Retinal Ganglion Cells - physiology
Retinal Neurons - physiology
Software
Synapses - physiology
Synaptic transmission
Testing
Theory
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Summary:The largest available cellular level connectivity map, of a 0.1 mm sample of the mouse retina Inner Plexiform Layer, was analysed using network models and visualized using spectral graph layouts and observed cell coordinates. This allows key nodes in the network to be identified with retinal neurons. Their strongest synaptic links can trace pathways in the network, elucidating possible circuits. Modular decomposition of the network, by sampling signal flows over nodes and links using the InfoMap method, shows discrete modules of cone bipolar cells that form a tiled mosaic in the retinal plane. The highest flow nodes, calculated by InfoMap, proved to be the most useful landmarks for elucidating possible circuits. Their dominant links to high flow amacrine cells reveal possible circuits linking bipolar through to ganglion cells and show an Off-On discrimination between the Left-Right sections of the sample. Circuits suggested by this analysis confirm known roles for some cells and point to roles for others.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0158626