title: Efficient physical embedding of topologically complex information processing networks in brains and computer circuits.
abstract: Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI) computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent's rule, which is related to the dimensionality of the circuit's interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent's rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks.
authors.affiliation: Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America. dbassett@physics.ucsb.edu
authors.forename: Danielle S
authors.initials: DS
authors.lastname: Bassett
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Greenfield
authors.forename: Andreas
authors.initials: A
authors.lastname: Meyer-Lindenberg
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Weinberger
authors.forename: Simon W
authors.initials: SW
authors.lastname: Moore
authors.forename: Edward T
authors.initials: ET
authors.lastname: Bullmore
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D017173:Caenorhabditis elegans; D017709:Fractals; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008322:Mammals; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer