In this lecture, we move on from generic Interacting Particle Systems (IPS) to the specific case of (deterministic) Cellular Automata (CA). We spend a lot of time on Elementary CA's (ECA) and the complex behaviors that they can generate, as cataloged by Wolfram's _A New Kind of Science_ (NKS). We discuss density classification, pattern recognition, and the mapping from CA's to (recurrent) neural networks (RNNs/ANNs). By showing that certain ECA rules (like "Rule 110" in particular) are Turing complete, we effectively show that Recurrent Neural Networks/Time Delay Neural Networks (TDNNs) are themselves Turing complete (i.e., they can execute any computable function). We also discuss a few basic 2D CA's. Throughout the lecture, NetLogo (Web) is used to demonstrate the CA's, which allows us to introduce agent-based modeling a bit (including a Particle Swarm Optimization (PSO) empirical example).
Whiteboard notes for this lecture can be found at: https://www.dropbox.com/s/gx54i8wt6gn2uow/IEE598-Lecture8B-2022-04-26-Elementary_Cellular_Automata-Computation-and-Experimentation.pdf?dl=0
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