Adaptive Networks and Bio-Inspired Cognition
Dr. Ali H. Sayed
University of California Los Angeles
Adaptive networks consist of spatially distributed agents that are linked together through a connection topology. The topology may vary with time and the agents may also move. The agents cooperate with each other through local interactions and by means of in-network processing. The diffusion of information across the network results in various forms of self-organizing behavior and collective intelligence. A key property of adaptive networks is that all agents behave in an isotropic manner and are assumed to have similar abilities. This kind of behavior is common in many socio-economic and life and biological networks where no single agent is in command. Adaptive networks are well-suited to perform decentralized information processing and decentralized inference tasks. They are also well-suited to model self-organizing behavior such as animal flocking and swarming. This talk describes research results on distributed processing over adaptive networks and illustrates the techniques by studying self-organization in biological networks such as bird formations, fish schooling, bee swarming, and bacteria motility.
A H. Sayed is Professor of Electrical Engineering and Director of the UCLA Adaptive Systems Laboratory. His research areas span adaptation and learning mechanisms, adaptive and cognitive networks, bio-inspired information processing, distributed and statistical signal processing. He has published 5 books and over 350 articles. His work received several awards including the 1996 IEEE Fink Prize, the 2003 Kuwait Prize, the 2005 Terman Award, and two Best Paper Awards from the IEEE Signal Processing Society (2002, 2005). He is a Fellow of IEEE and served as Editor-in-Chief of the IEEE Transactions on Signal Processing (2003-2005) and as 2005 Distinguished Lecturer of the IEEE SP Society. He is currently serving as Vice-President of Publications of the same Society.