Structural characteristics of local cortical networks wired by distance dependent connectivity rules

This dataset contains the results of a computational study investigating local connectivity in the cerebral cortex. Distance-dependent (DD) connectivity rules are compared with configuration-model (CM) networks, in which spatial relationships between neurons are randomized while preserving each neuron’s in- and out-degree.We model a two-dimensional square sheet of 101 × 101 pyramidal neurons (10,201 total). Neurons are indexed from left to right, row by row, starting at the top left. In all data files, a directed connection is written as “presynaptic → postsynaptic” (e.g., 54->8576).Distance-dependent connectivityDD networks are based on Gaussian distance-dependent profiles:Two domain-specific scenarios were implemented:Anatomical scenarioconnection probability at zero distance: 0.8.standard deviation of the Gaussian kernel: systematically varied from 1 to 10 (integers)Electrophysiological scenarioConnectivity profiles were derived from the following studies:Holmgren et al. (2003), J. Physiology 551(Pt 1), 139–153. Levy & Reyes (2012), J. Neuroscience 32(16), 5609–5619. Perin et al. (2011), PNAS 108(13), 5419–5424.  Files included: DD networksnetworkDDanatomy – anatomical scenarionetworkDDholmgren – based on Holmgren et al. (2003)networkDDlevyandreyes – based on Levy & Reyes (2012)networkDDperin – based on Perin et al. (2011)Configuration-model networksConfiguration-model (CM) networks preserve the in-degree and out-degree of each neuron from their DD counterparts, but otherwise connections are randomized.Corresponding CM files:networkCManatomynetworkCMholmgrennetworkCMlevyandreyesnetworkCMperinFile formatAll networks are provided as .rtf files with readable directed-edge lists.