This Workshop is supported by the NSF Award 0456419 by OISE International Plan & Workshops program.
The aim of the workshop is to expose the participants to the newest methods available for the description and analysis of large-scale, complex structures using methods of combinatorics and graph theory, in combination with nonlinear, adaptive systems theory, and statistical physics. The study of mesoscopic (intermediate-range) effects is motivated by hierachical brain models, which span microscopic and macroscopic scales. The Workshop will cover the following areas: overview of the present state of research on large-scale random graphs in various fields of science, from physics to neurobiology and cognitive science; identification the problems in computation theory, brain science and statistical physics that require the development of new random graph methods; detailed discussion of the existing methods and outlining potential new theories to address those problems; application of combinatorial tools to give heuristics and, whenever possible, solve hard problems in various fields; outline avenues of practical application of the novel methods, especially in models of biological and artificial neural networks and brains, physical systems, and also complex networks relevant to communication and information theory.
The Research Program will include the following major themes:
Scale-free random graphs and complex real-world networks;
Areas of interest include but limited to: www, chemical networks, social networks, biological systems, neural populations;
Phase transitions and threshold phenomena in random media;
Random bootstrap percolation, cellular automata, and neural network;
Non-convergent neurodynamics for modeling perception, memory, and intention.
Materials of the workshop are to be published in a special issue/edited volume to be specified at a later stage.
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