We present a self-organizing Neural Cellular Au- tomaton (NCA) equipped with dynamic, learnable graph connec- tivity that adapts its computational topology in response to input history. The system exhibits history-dependent behavior: distinct input sequences induce divergent graph structures, enabling memory without explicit recurrence. We interpret the NCA as a fixed physical substrate (a lattice of identical units) that dynamically reconfigures its functional circuitry through edge modulation. We further propose a novel harmonic decoupling mechanism—mapping node activations to musical chords and traversing the circle of fifths between timesteps—to maximally decorrelate sequential representations, thereby reducing interfer- ence and enabling long-range communication across the graph. We argue that scaling such a system to brain-like dimensions (∼ 1011 nodes) with this decoupling paradigm could yield human- level learning efficiency, as the graph learns to specialize local neighborhoods for distinct computational roles. Experimental results on sequence transformation tasks confirm that different inputs follow distinct graph-evolution trajectories, demonstrating emergent memory bias and dynamic specialization.

We have demonstrated a self-organizing NCA that learns history-dependent tasks by dynamically reconfiguring its graph structure. Results confirm that different input sequences follow distinct graph-evolution trajectories, implementing memory through morphogenetic specialization. By interpreting the system as a fixed substrate with learned functional topology, and augmenting it with harmonic de- coupling via the circle of fifths, we outline a path toward brain-scale artificial systems that match biological learning efficiency. The dynamic NCA is not just a model—it is a computational morphology engine, where intelligence emerges from the dance of structure and state.

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