Logic and learning in network cascades

Abstract Critical cascades are found in many self-organizing systems. Here, we examine critical as a design paradigm for logic and learning under the linear threshold model (LTM), simple biologically inspired variants of it sources computational power, efficiency, robustness. First, show that LTM can compute logic, with small modification, universal Boolean examining its stability cascade frequency. We then frame formally binary classifier remark on implications accuracy. Second, statistical model, studying benefits spatial constraints criticality to efficiency. also discuss robustness information encoding. Our experiments greatly increase Theoretical investigation initial experimental results indicate result sudden