89 citations · 106 across the 12 of their papers we have counts for
12 papers
Self-Organising Digital Circuits
Marcello Barylli, Gabriel Béna, Alexander Mordvintsev +2
Fault tolerance in classical computing has traditionally relied on static strategies like hardware redundancy and error-correcting codes. Biological systems, in contrast, exhibit a…
Visualising the Attractor Landscape of Neural Cellular Automata
James Stovold, Mia-Katrin Kvalsund, Harald Michael Ludwig +2
As Neural Cellular Automata (NCAs) are increasingly applied outside of the toy models in Artificial Life, there is a pressing need to understand how they behave and to build approp…
Differentiable Logic Cellular Automata: From Game of Life to Pattern Generation
Pietro Miotti, Eyvind Niklasson, Ettore Randazzo +1
This paper introduces Differentiable Logic Cellular Automata (DiffLogic CA), a novel combination of Neural Cellular Automata (NCA) and Differentiable Logic Gates Networks (DLGNs).…
Neural Cellular Automata: From Cells to Pixels
Ehsan Pajouheshgar, Yitao Xu, Ali Abbasi +3
Neural Cellular Automata (NCAs) are bio-inspired dynamical systems in which identical cells iteratively apply a learned local update rule to self-organize into complex patterns, ex…
Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction
Blaise Agüera y Arcas, Jyrki Alakuijala, James Evans +5
The fields of Origin of Life and Artificial Life both question what life is and how it emerges from a distinct set of "pre-life" dynamics. One common feature of most substrates whe…
Mesh Neural Cellular Automata
Ehsan Pajouheshgar, Yitao Xu, Alexander Mordvintsev +3
Texture modeling and synthesis are essential for enhancing the realism of virtual environments. Methods that directly synthesize textures in 3D offer distinct advantages to the UV-…
Biomaker CA: a Biome Maker project using Cellular Automata
Ettore Randazzo, Alexander Mordvintsev
We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA). In Biomaker CA, morphogenesis is a first class citizen and small seeds need to grow into plant-like or…
Differentiable Programming of Chemical Reaction Networks
Alexander Mordvintsev, Ettore Randazzo, Eyvind Niklasson
We present a differentiable formulation of abstract chemical reaction networks (CRNs) that can be trained to solve a variety of computational tasks. Chemical reaction networks are…
Growing Steerable Neural Cellular Automata
Ettore Randazzo, Alexander Mordvintsev, Craig Fouts
Neural Cellular Automata (NCA) models have shown remarkable capacity for pattern formation and complex global behaviors stemming from local coordination. However, in the original i…
Transformers learn in-context by gradient descent
Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo +4
At present, the mechanisms of in-context learning in Transformers are not well understood and remain mostly an intuition. In this paper, we suggest that training Transformers on au…
Growing Isotropic Neural Cellular Automata
Alexander Mordvintsev, Ettore Randazzo, Craig Fouts
Modeling the ability of multicellular organisms to build and maintain their bodies through local interactions between individual cells (morphogenesis) is a long-standing challenge…
NCA: Texture Generation with Ultra-Compact Neural Cellular Automata
Alexander Mordvintsev, Eyvind Niklasson
We study the problem of example-based procedural texture synthesis using highly compact models. Given a sample image, we use differentiable programming to train a generative proces…