Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits

By D. Bassett, Daniel Greenfield, Andreas Meyer-Lindenberg, Daniel R. Weinberger, Simon W. Moore, E. Bullmore. PLoS Comput. Biol., 2010

407 citations recorded by the source corpus.

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.

Subfields: Aging, Cognitive Neuroscience, Plant Science

View Paper: https://www.semanticscholar.org/paper/bc33d793d4b33b5448f2d98da9ed6fc30a2c8bd0

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Citations in the corpus

Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks (2014, background): “In terms of global information flow statistics, the macaque network was found to be intermediate to its latticized and randomized reference networks, mirroring the notion that the complex topology of structural networks represents a trade-off between wiring cost and communication efficiency for inte”
Uncovering space-independent communities in spatial networks (2010, background): “…or ad-hoc reasons [36, 37, 38, 39]; ii) the modeling of historical or pre-historical interactions based on limited archaeological evidence [40, 41]; iii) the identiﬁcation of functionally related brain regions and of principles leading to global integration and functional segregation [42, 43].”
Modular and Hierarchically Modular Organization of Brain Networks (2010, background): “Bassett et al. (2010) measured hierarchical modularity of the cellular connectome of the nematode Caenorhabditis elegans and compared this nervous system to whole brain anatomical networks in human MRI and DTI data.”
Generative models of the human connectome (2015, unknown): “Network analyses of the connectome have revealed a host of attributes that are likely essential for healthy brain function, including hierarchical and multi-scale modules (Bassett et al., 2010; Betzel et al., 2013), highly connected, highly central hubs (Hagmann et al.”
Conserved and variable architecture of human white matter connectivity (2011, unknown): “While Rentian scaling has been reported to exist in a small sample of DSI networks for a single template and resolution (Bassett et al., 2010), here we report for the first time the robust presence of this phenomena across imaging modality (DSI or DTI), atlas, and spatial resolution; see Fig.”
Griffiths phases and the stretching of criticality in brain networks (2013, background): “In this case too, the network is hierarchical [22, 33, 42].”
Networks of the Brain: Quantitative Analysis and Modeling (2010, unknown): “This architectural feature promotes economical physical embedding (Bassett et al., 2010) and may have significant implications for brain dynamics (Kaiser et al.”
Cognitive Network Neuroscience (2015, background): “It is important to note that all parcellation schemes can be defined across a range of spatial resolutions, enabling the exploration of multiresolution structure in the human brain (Bassett, Brown, et al., 2011; Bassett et al., 2010; Zalesky et al., 2010; Meunier et al., 2009).”
Closures and Cavities in the Human Connectome (2016, background): “Macroscopic computation and cognition in the human brain are affected by an intricately interconnected collection of neurophysical mechanisms [4, 58].”
Modular Brain Networks (2015, methodology): “…revealing known functional groups (Pan et al. 2010, Sohn et al. 2011) or novel network properties, such as cores (Pavlovic et al. 2014), the roles of hub nodes (Towlson et al. 2013), and the hierarchical organization of communities nested within communities (Bassett et al. 2010, Sohn et al. 2011).”
A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks (2011, background): “Although brain networks show small-world properties, several experimental studies have also shown that they are hierarchical, fractal and highly modular (2, 3, 18).”