Reference library

Self-Organising Textures

Paper summary

Trains neural cellular automata against texture and feature targets, producing persistent evolving patterns. Analyses examine hidden states, robustness, and unexpected properties of the learned local rules.

Relevance to the project

A comparison for sustained spatial dynamics and shared GPU-friendly mechanisms. Separate appearance, target-matching training, persistence, and functional adaptation.

Neural cellular automataAggregation and self-assemblySelf-maintenanceCellular automata

Dynamic visual texture alone is not a life or learning measurement.

Source record

Reference ID: article-https-distill-pub-selforg-2021-textures
Archived version: Cited online article
Summary provenance: reference-library/papers/article-https-distill-pub-selforg-2021-textures/metadata.json · summary
Review status: initial orientation summary; not a critical full-paper review
Reading scope: Selected abstract/opening and available closing discussion/conclusion passages in the preserved text extract; broader project notes provide context. No reproduction of experiments, exhaustive methods review, or equation/figure audit.