Learning and Memory in Energy-Efficiency Theory: The Consolidation and Dissipation of Constrained-State Energy Texture

Learning and memory are central to cognition, yet traditional theories describe synaptic plasticity and memory consolidation without answering the ontological question: what is learning, and what is the physical nature of memory? This paper develops an interpretation within Energy-Efficiency Theory (EET). Starting from Yang's Axioms and the entropy decomposition S=Sc+Sf−ScorrS=Sc+Sf−Scorr, we propose that learning is the inverse-entropy consolidation of constrained-state energy texture, memory is the consolidated constrained-state energy texture, and forgetting is the natural dissipation of constrained-state entropy. We derive the learning rate from the inverse entropy formula S˙inv,learn=Pfree/TS˙inv,learn=Pfree/T (valid under steady-state cognitive conditions with Δt≥Δtmin⁡Δt≥Δtmin), establish the memory strength dynamics dFmemdt=S˙inv,learn−λmemFmemdtdFmem=S˙inv,learn−λmemFmem, and distinguish passive from active forgetting within the EET framework. The paper connects to companion works including Information Is Not a Substance, Inertia Does No Work, The Ontology of Time, The Cognitive Buffer, The Ontology of Inverse Entropy, The Neural Buffer, The Ontology of Consciousness, and The Ontology of Spirit, forming a closed-loop theoretical system. Testable predictions with statistical criteria are provided.