Cellular Automata as Convolutional Neural Networks


Deep learning techniques have recently demonstrated broad success in predicting complex dynamical systems ranging from turbulence to human speech, motivating broader questions about how neural networks encode and represent dynamical rules. The authors explore this problem in the context of cellular automata (CA), simple dynamical systems that are intrinsically discrete and thus difficult to analyze using standard tools from dynamical systems theory. They show that any CA may readily be represented using a convolutional neural network with a network-in-network architecture.


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