The Intelligence of Matter: Quantized Energy-Information Laws in Electrodynamic (EDI) Systems

This work presents a physics-based framework for quantifying intelligence capacity in Electrodynamic Intelligence (EDI) systems, such as biological neural networks or artificial memristive arrays. We introduce two complementary models, a thermodynamic-electrostatic model and a memory-energy model, and unify them through ionic and electric field dynamics. Intelligence capacity is linked to physical characteristics of materials, network coupling, and energy constraints. The Electrodynamic Intelligence Quotient (Q), a dimensionless metric unifies thermodynamic and memory-energy models through the fundamental properties of a physical substrate. Intelligence capacity is shown to be a function of material properties, network coupling, and energy constraints. When this threshold is surpassed, the system exhibits emergent, quantized intelligence, analogous to a phase transition. For systems where Q < 1, the physical substrate only supports passive memory storage; however, when Q ≥ 1, a phase transition occurs, generating self-learning and active memory consolidation. Grounded in both theoretical derivations and proposed experimental validations, this framework provides a foundation for engineering physically embodied intelligence in matter.