Small world networks in computational neuroscience

Dmytro Korenkevych, Jui Hong Chien, Jicong Zhang, Deng Shan Shiau, Chris Sackellares, Panos M. Pardalos

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Interest in network sciences has greatly increased over the last decades. Network models were recognized as a crucial tool in analyzing dynamics of complex systems. Small-world networks are apparently ubiquitous in nature and possess some special properties which make them highly suitable to model the brain. In this survey the theory of small-world networks and their applications in computational neuroscience is reviewed. First formal definitions and properties of small-world networks and how different network characteristics relate to each other are discussed. An overview of brain network models including anatomical network models and functional network models is given, and general steps of building these models are discussed. Different techniques of brain data acquisition are considered and a brief overview of computational methods of analysis these data is given. Finally, research works that have been done on neural data and reported existence of small-world networks in brain are reviewed.

Original languageEnglish (US)
Title of host publicationHandbook of Combinatorial Optimization
PublisherSpringer New York
Pages3057-3088
Number of pages32
Volume5-5
ISBN (Electronic)9781441979971
ISBN (Print)9781441979964
DOIs
StatePublished - Jan 1 2013
Externally publishedYes

ASJC Scopus subject areas

  • Mathematics(all)

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