Ring–Field Neuromorphic Core (RFNC) Moving-Hamiltonian Coupling with Global Coherence Field: Diagrams and Simulation Pseudocode

This work introduces the Ring Field Neuromorphic Core (RFNC) — a physically grounded, relational neuromorphic architecture designed to maintain long-term coherence in adaptive systems without static pinning, hard resets, or global supervision.

RFNC is formulated as a moving Hamiltonian system with a global coherence field, coupling fast oscillatory dynamics (ring fields) with slow structural variables through explicitly defined feedback loops. The architecture is intended for neuromorphic and hybrid hardware, but is presented in a device-agnostic formalism, making it applicable across multiple physical substrates.

The core contributions of this work include:

• A complete mathematical specification of RFNC dynamics, including state variables, evolution equations, and coupling terms.

• A formal coherence functional acting as a global stabilizer, enabling recovery from perturbations while avoiding convergence to rigid attractors.

• Explicit separation of timescales (fast oscillatory processes vs. slow structural adaptation), allowing sustained learning without catastrophic drift.

• A Lyapunov-based stability argument demonstrating boundedness and practical stability under noise and component aging.

• A set of falsifiable experimental protocols, including shock-recovery tests, aging trajectories, and energy–retention trade-off measurements.

• A mapping from abstract dynamics to realistic physical proxies (e.g. MTJ/TMR variability, oscillator noise, device aging), enabling empirical validation.

RFNC does not propose a new ontology of intelligence or cognition. Instead, it addresses a concrete engineering problem: how to preserve adaptive coherence in systems operating far from equilibrium, where static control strategies fail.

The framework is compatible with existing empirical knowledge and does not replace established models; rather, it provides a relational control layer capable of integrating heterogeneous components into a stable, learning system.

This document is not a conceptual manifesto. It is a closed technical specification, suitable for simulation, implementation, and falsification.

Under Creative Commons BY-NC-ND 4.0 license