A. Pal et al. (Eds.): IWDC 2005, LNCS 3741, pp. 111 – 116, 2005. © Springer-Verlag Berlin Heidelberg 2005 The Brain, Complex Networks, and Beyond L.M. Patnaik Indian Institute of Science, Bangalore 560012 lalit@micro.iisc.ernet.in (Prof. A K Choudhury Memorial Lecture) Abstract. This presentation covers a synthesizing overview of the structural organisation of the brain, viewed as a complex network. Such an organisation is encountered in social, information, technological, and biological networks. The underlying conclusions may, in future, lead to interesting studies in the areas of cognition, and distribution computing. It is also hoped that the brain network structure studied through scale-free, small world, and clustering concepts may facilitate better understanding and design of brain-computer interface (BCI) systems. 1 Introduction I deem it an honour to deliver the Prof. A K Choudhury Memorial Lecture at the Seventh International Workshop on Distributed Computing. Prof. Choudhury was an outstanding researcher who has made pioneering contributions in diverse areas in the broad discipline of Electrical Sciences. Notable among the areas where he made some of his excellent contributions are, control and system theory, fault diagnosis, computer hardware and logic design, network and circuit theory. As a befitting tribute to this great scholar, I have chosen a topic of interdisciplinary nature covering some of the above areas. Networks in the human brain possibly work similar to those in the internet. Networks often have very many nodes with very few links, and very few nodes with very many links. The brain is one of the most challenging complex systems. The neurons are massively interconnected to each other. To understand the complexity of the nervous system, we need to characterize its network structure. Networks are described by simply defining a set of nodes and connections (edges) between them. A wide variety of such systems are scale-free, where the connectivity distribution takes a power-law form. What makes such networks complex is not only their size but also the interaction of architecture or the interconnection topology and dynamics. In many networks, cluster of nodes group into tightly coupled neighborhoods, but maintain short distances among nodes in the entire network. Such a situation leads to what is known as ‘small world’ within the network [1]. For many networks, the degree of individual nodes forms a distribution that decays as a power law, producing a ‘scale- free architecture’ characterized by highly connected nodes (hubs).