  This paper introduces a scale-invariant methodology employing \textit{Fractal
Geometry} to analyze and explain the nonlinear dynamics of complex
connectionist systems. By leveraging architectural self-similarity in Deep
Neural Networks (DNNs), we quantify fractal dimensions and \textit{roughness}
to deeply understand their dynamics and enhance the quality of
\textit{intrinsic} explanations. Our approach integrates principles from Chaos
Theory to improve visualizations of fractal evolution and utilizes a
Graph-Based Neural Network for reconstructing network topology. This strategy
aims at advancing the \textit{intrinsic} explainability of connectionist
Artificial Intelligence (AI) systems.
