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Variational Neural Cellular Automata
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In nature, the process of cellular growth and differentiation has lead to an amazing diversity of organisms -- algae, starfish, giant sequoia, tardigrades, and orcas are all created by the same generative process. Inspired by the incredible diversity of this biological generative process, we propose a generative model, the Variational Neural Cellular Automata (VNCA), which is loosely inspired by the biological processes of cellular growth and differentiation. Unlike previous related works, the VNCA is a proper probabilistic generative model, and we evaluate it according to best practices. 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. While there is a significant gap to the current state-of-the-art in terms of generative modeling performance, we show that the VNCA can learn a purely self-organizing generative process of data. Additionally, we show that the VNCA can learn a distribution of stable attractors that can recover from significant damage.
Forward citations
Cited by 5 Pith papers
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Transient State Reorganization and Cell Differentiation in the Developmental Dynamics of Growing Neural Cellular Automata
GNCA development proceeds through transient state reorganization—overshooting morphology, self-organizing channels, and expanding/contracting cell-type communities—rather than monotonic refinement.
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Architecture Generalization with MetaNCA
A learned local rule (Weight Transformer) iteratively self-organizes task-network weights from local graph neighborhoods and generalizes across unseen MLP, CNN, and ResNet architectures up to ~2M parameters.
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Mixtures of Neural Cellular Automata: A Stochastic Framework for Growth Modelling and Self-Organization
Mixture of Neural Cellular Automata (MNCA) adds a learned categorical rule selector and intrinsic Gaussian noise to NCA, improving perturbation robustness and yielding interpretable rule segmentation.
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Missing Data Imputation using Neural Cellular Automata
NICA, an iterative self-attention model styled after cellular automata, achieves lower mean RMSE than Mean, KNN, MICE, and GAIN on 14 of 15 tabular datasets with simulated MCAR missingness.
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Neural cellular automata: applications to biology and beyond classical AI
A review arguing that trainable cellular automata form a unifying, computationally lean paradigm for biology-inspired collective intelligence and generative AI.
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