THE EL-RAKHAWI FRAMEWORK FOR NEURO-PHYSICAL COGNITIVE DYNAMICS: UNIFYING INFORMATION GEOMETRY, NON-EQUILIBRIUM THERMODYNAMICS, AND AI FOR BRAIN-LIKE CONTINUOUS LEARNING

Abstract

The El-Rakhawi Framework for Neuro-Physical Cognitive Dynamics (NPCD) addresses catastrophic forgetting and thermodynamic inefficiency in AI by modeling cognitive states as dynamic attractor basins within Riemannian manifolds, governed by non-equilibrium thermodynamics and the Free Energy Principle. Unlike traditional deep learning's static matrix operations, NPCD integrates four modules: Information Geometry using Fisher Information Metric for natural gradient flow; Non-Equilibrium Thermodynamic Learning via Fokker-Planck dynamics; Criticality-Maintained Neural Fields operating at the edge of chaos; and Continuous Attractor Integration Protocol growing new basins in unoccupied phase-space regions, mathematically resolving catastrophic forgetting. The dimensionally consistent NPCEM metric yields bits per Joule, directly comparable to Landauer's limit. Applications include lifelong learning AI, adaptive brain-computer interfaces, and ultra-low-power neuromorphic computing, establishing a unified foundation bridging information geometry, statistical physics, and artificial intelligence.