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Proof of concept: a spatial modular small-world self-organises by adaptive rewiring

  • 1, 2Email author,
  • 1,
  • 1,
  • 2, 3 and
  • 2
BMC Neuroscience201516 (Suppl 1) :P272

https://doi.org/10.1186/1471-2202-16-S1-P272

  • Published:

Keywords

  • Connectivity Structure
  • Spatial Layout
  • Brain Connectivity
  • Cortical Connectivity
  • Information Processing Capacity
A small-world network is a network that reconciles two opposing properties, segregation and integration. It is this reconciliation that gives rise to the impressive information processing capacity of the human brain; segregation provides a platform for information processing, whilst integration provides for the fast transmission of information. However, the connectivity structure of the brain is not static [1]; it changes on multiple time-scales; on a relatively fast time-scale, synaptic plasticity takes place, whilst on a slower time-scale there is rewiring of brain connectivity through growth of axons and dendrites. This structural plasticity depends on the even faster time-scale of neural activity. But the relationship is symbiotic: patterns of synchronous activity are, of necessity, mediated by the brain connectivity structure. Gong & van Leeuwen [2] showed that rewiring of an initially random network - adaptive rewiring - in a model of spontaneous cortical activity gives rise to a particular type of network connectivity structure: a modular small-world. In order to improve the applicability of such a model to the cortex, spatial characteristics of cortical connectivity need to be respected. For this purpose we consider networks endowed with a metric by embedding them into a physical space. Such spatial constraints may represent wiring and metabolic costs in the brain. We provide an adaptive rewiring model with a spatial distance function and a corresponding spatially local rewiring bias [3].
Figure 1
Figure 1

A, Network adjacency matrix organised to optimise visual presentation of modular structure. B, Units on the sphere colour-coded to identify distinct modules.

Conclusion

The resulting rewiring scenarios showed a spatial layout of the connectivity structure, in which topologically segregated modules correspond to spatially segregated regions, and these regions are linked by long-range connections (see Figure 1, A and B). Greater realism and increased efficiency and robustness of the symbiosis of activity and structure is achieved compared to non-spatial adaptive rewiring. Thus, the principle of locally biased adaptive rewiring may explain both the topological connectivity structure and spatial distribution of connections between neuronal units in a large-scale cortical architecture.

Authors’ Affiliations

(1)
Perceptual Dynamics Laboratory, University of Leuven, Leuven, Flemish Brabant, B3000, Belgium
(2)
Department of Mathematics, University of Leicester, Leicester, LE1 7RH, UK
(3)
Saint-Petersburg State Electrotechnical University, Saint-Petersburg, Saint Petersburg, 197376, Russia

References

  1. Zhang LI, Poo MM: Electrical activity and development of neural circuits. Nat Neurosci. 2001, 4: 1207-1214.PubMedView ArticleGoogle Scholar
  2. Gong P, van Leeuwen C: Evolution to a small-world network with chaotic units. Europhys Lett. 2004, 67: 328-333.View ArticleGoogle Scholar
  3. Jarman N, Trengove C, Steur E, Tyukin I, v. Leeuwen C: Spatially constrained adaptive rewiring in cortical networks creates spatially modular small world architectures. Cognitive Neurodynamics. 2014, 8: 479-497.PubMedPubMed CentralView ArticleGoogle Scholar

Copyright

© Jarman et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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