title: A simple generative model of the mouse mesoscale connectome.
abstract: Recent technological advances now allow for the collection of vast data sets detailing the intricate neural connectivity patterns of various organisms. Oh et al. (2014) recently published the most complete description of the mouse mesoscale connectome acquired to date. Here we give an in-depth characterization of this connectome and propose a generative network model which utilizes two elemental organizational principles: proximal attachment ‒ outgoing connections are more likely to attach to nearby nodes than to distant ones, and source growth ‒ nodes with many outgoing connections are likely to form new outgoing connections. We show that this model captures essential principles governing network organization at the mesoscale level in the mouse brain and is consistent with biologically plausible developmental processes.
authors.affiliation: Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, United States.|Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom.
authors.forename: Sid
authors.identifier: http://orcid.org/0000-0002-4335-4218
authors.initials: S
authors.lastname: Henriksen
authors.affiliation: Graduate Program in Neuroscience, University of Washington, Seattle, United States.
authors.forename: Rich
authors.identifier: http://orcid.org/0000-0002-2644-6110
authors.initials: R
authors.lastname: Pang
authors.affiliation: Graduate Program in Neuroscience, University of Washington, Seattle, United States.
authors.forename: Mark
authors.identifier: http://orcid.org/0000-0002-6521-3256
authors.initials: M
authors.lastname: Wronkiewicz
mesh_terms: D000818:Animals; D063132:Connectome; D051379:Mice; D016571:Neural Networks, Computer
keywords: computational biology; graph theory; mesoscale; mouse; mouse connectome; network analysis; neuroscience; proximal attachment; source growth; systems biology