Sensor movement drives emergent attention and scalability in active neural cellular automata

The brain's distributed architecture has inspired numerous artificial intelligence (AI) systems, particularly through its neocortical organization. However, current AI approaches largely overlook a crucial aspect of biological intelligence: active sensing - the deliberate movement of sensory organs to explore the environment. To explore how sensor movement impacts behavior in image classification tasks, we introduce the Active Neural Cellular Automata (ANCA), a neocortex-inspired model with movable sensors. Active sensing naturally emerges in the ANCA, with belief-informed exploration and attentive behavior to salient information, without adding explicit attention mechanisms. We show that active sensing simplifies classification tasks. Moreover, active sensing lets the ANCA be smaller than the image size without losing information, which makes it highly scalable. We show that the ANCA maintains over 90% accuracy zero-shot as the system size is increased or decreased on a 3-class MNIST task. This scalability enables fault tolerance on the same task, maintaining over 90% accuracy with up to 70% silenced sensors, a scenario where traditional architectures fail. Overall, our work provides insight to how distributed architectures can interact with movement, opening new avenues for adaptive AI systems in embodied agents.