Variational Neural Cellular Automata

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Neural and Evolutionary Computing

Variational Neural Cellular Automata

We propose a new generative model, the variational neural cellular automata (vnca), which is loosely inspired by the biological processes of cellular growth and differentiation.

We find that the vnca learns to reconstruct samples well and that despite its relatively few parameters and simple local-only communication, the vnca can learn to generate a large variety of output from information encoded in a common vector format.

We show that the vnca can learn a purely self-organizing generative process of data and that it can learn a distribution of stable attractors that can recover from significant damage.