User:Neuroam/Small-world network

(Introduction) The basis of the small-world theory was originally proposed in 1967 by Stanley Milgram in his paper, "The Small-World Problem". Milgram's experiment measured path length in social networks and found that an it took an average of 6 connections for a letter to travel from Omaha, Nebraska to Boston, Massachusetts. The finding introduced the idea that information can travel great distances with shorter path length via interconnected nodes. .... (Watts and Strogatz)

Small-world neural networks in the brain[edit]
Structural and functional connectivity in the brain has also been found to reflect the small-world topology of short path length and high clustering. The network structure has been found in the mammalian cortex across species as well as in large scale imaging studies in humans. Advances in connectomics and network neuroscience, have found the small-worldness of neural networks to be associated with efficient communication.

In neural networks, short pathlength between nodes and high clustering at network hubs supports efficient communication between brain regions at the lowest energetic cost. The brain is constantly processing and adapting to new information and small-world network model supports the intense communication demands of neural networks. High clustering of nodes forms local networks which are often functionally related. Short path length between these hubs supports efficient global communication. This balance enables the efficiency of the global network while simultaneously equipping the brain to handle disruptions and maintain homeostasis, due to local subsystems being isolated from the global network. Loss of small-world network structure has been found to indicate changes in cognition and increased risk of psychological disorders.