BraiNCA: brain-inspired neural cellular automata and applications to morphogenesis and motor control
Most of the Neural Cellular Automata (NCAs) defined in the literature have a common theme: they are based on regular grids with a Moore neighborhood (one-hop neighbour). They do not take into account long-range connections and more complex topologies as we can find in the brain. In this paper, we introduce BraiNCA, a brain-inspired NCA with an attention layer, long-range connections and complex topology. BraiNCAs shows better results in terms of robustness and speed of learning on the two tasks compared to Vanilla NCAs establishing that incorporating attention-based message selection together with explicit long-range edges can yield more sample-efficient and damage-tolerant self-organizat...
| Authors | Léo Pio-Lopez, Benedikt Hartl, Michael Levin |
|---|---|
| Primary category | cs.AI |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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