How Does the Brain Organize Information?

TL;DR

This paper explores how the brain organizes information through neural network models, emphasizing the role of small-world and scale-free connectivity architectures in self-organization and cognitive processes.

Contribution

It discusses the application of neural network models, especially Kohonen networks, to understand brain information organization and highlights the significance of small-world and scale-free architectures.

Findings

Evidence of small-world and scale-free connectivity in brain networks

Neural network models explain associative memory and seizure prediction

Connectivity architecture influences learning and classification

Abstract

Cognitive processes in the brain, like learning, formation of memory, recovery of memorized images, classification of objects have two features: First, there is no supervisor in the brain who controls these processes. Second there is a hugh number of neurons (10^{6} to 10^{10}) involved in those cognitive tasks. For this reason, the search of understanding cognitive processes uses models built from a large number of neurons, but very much simplified neurons. The so-called neural networks have been quite successful in describing certain aspects of brain functions, like the mechanism of associative memory or recently the prediction of epileptic seizures. At hand of the Kohonen network we discuss the treatment of information in the brain, in particular how the brain organizes such information without supervisor. Recently, networks of small-world and scale-free architecture came into focus. There is evidence indicating that the brain (cat cortex, macaque cortex, human brain) uses such connectivity architecture. Tasks like treatment of information, learning and classification take advantage of such scale-free and small-world connectivity and thus play a potentially important role in self-organization of the brain.