In this lecture, we continue discussing Interacting Particle Systems (IPS) and complex systems approaches for understanding adaptive properties of distributed systems, such as neural networks. We specifically focus on cellular automata (CA), starting with 2D cases (such as models of fire spread, the Game of Life, and "Brian's Brain" from NetLogo) and then moving to Elementary Cellular Automata (ECA). We discuss some popular ECA rules, such as the traffic rule (184) and the majority rule (rule 232) and how they can be combined to do density classification. We also discuss how rule 110 is Turing complete and thus can do much more.
Whiteboard notes for this lecture can be found at:
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