A Framework for Structural Geometric Convergence Between Human DNA and Neural Networks: Integrating Cell-Published Chromatin Data with Independent Empirical Measurements

This work presents the first systematic framework for comparing structural organization patterns between human chromatin architecture and artificial neural networks. By integrating recently published MCCu (Micro Capture-C ultra) data from Li et al. (Cell, 2025) with independent empirical measurements from a self-organizing neural network, we demonstrate striking geometric convergences that cannot be attributed to chance. Both systems exhibit analogous structural signatures: Diagonal dominance reflecting local coupling (nucleosome contacts / layer self-correlation) Horizontal and vertical banding patterns indicating hub-mediated communication (enhancer-promoter contacts / hub-layer coupling) Hierarchical substructures within primary domains (nanoscale TAD formation / awareness-resonance layer clustering) Statistically significant correlation distributions (p