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. 2011 Jan 11;6(1):e16113. doi: 10.1371/journal.pone.0016113

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Cahalane et al.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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(A) We begin with a grid of 100µm squares, each containing a node located uniformly at random within the square. For each node, a set of candidate end points for efferent links is drawn from under the probability distribution, those terminating outside the boundary of the region (such as the dashed lines here) will be discarded in favor of new candidates until the quota of n_axons has been reached. The ends of the candidate axons snap to the nearest node, and the links are recorded in the adjacency matrix. (B) Repeating the procedure in (A) for each node results in a network such as that depicted here. For clarity of display, the network in (B) is drawn on a grid of nodes having n_axons = 10. Networks used in our simulation have and n_axons = 10.

Inline graphic — (A) We begin with a grid of 100µm squares, each containing a node located uniformly at random within the square. For each node, a set of candidate end points for efferent links is drawn from under the probability distribution, those terminating outside the boundary of the region (such as the dashed lines here) will be discarded in favor of new candidates until the quota of n_axons has been reached. The ends of the candidate axons snap to the nearest node, and the links are recorded in the adjacency matrix. (B) Repeating the procedure in (A) for each node results in a network such as that depicted here. For clarity of display, the network in (B) is drawn on a grid of nodes having n_axons = 10. Networks used in our simulation have and n_axons = 10.