Abstract
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.
Highlights
Detailed data on synaptic connections between nerve cells is becoming available [1,2,3]
Three basic measures are summarised in S1 and S3 Tables; these are node Degree (k), node Betweeness Centrality, and link or edge Betweenness Centrality
The highest Degree nodes (S1 Table), of the un-weighted network sums the number of synaptic links incident on a given neuron, while for the weighted network this sum tallies the weight of each link
Summary
Detailed data on synaptic connections between nerve cells is becoming available [1,2,3]. This provides hope that the structure of specific neural circuits can be determined and related to the underlying fabric of neurons and synaptic connections. Such could provide a platform for interpreting the available functional probes [4] and elucidate and eventually understand their functions within brain systems. Visualization of networks via a computer graphics drawing can illustrate structure and functions as well as providing insights into information processing circuits.
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