Small World Networks in Computational Neuroscience

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.