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The relative efficiency of modular and non-modular networks of different size

Lookup NU author(s): Dr Colin Tosh



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: ‘small’ and ‘large’, and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity.

Publication metadata

Author(s): Tosh CR, McNally L

Publication type: Article

Publication status: Published

Journal: Proceedings of the Royal Society of London B: Biological Sciences

Year: 2015

Volume: 282

Issue: 1802

Print publication date: 01/03/2015

Online publication date: 28/01/2015

Acceptance date: 23/12/2014

Date deposited: 18/12/2014

ISSN (print): 0962-8452

ISSN (electronic): 1471-2954

Publisher: The Royal Society Publishing


DOI: 10.1098/rspb.2014.2568


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Funder referenceFunder name
095831Wellcome Trust
NE/H015469/1UK Natural Environment Research Council (NERC)