Neural Resonance Architecture: Toward Self-Regulating Energy-Efficient Intelligence

The Neural Resonance Architecture (NRA) is proposed as a biologically inspired
computational framework that combines oscillatory information processing, adaptive
precision control, and energy-efficient learning. Unlike traditional deep neural networks that
use the same precision and maximum energy for all activation pathways, the NRA breaks
information into resonance bands. Each band operates at a balanced level of energy and
precision. This model reflects the cortical oscillations and hierarchical resonance dynami
seen in neuroscience, while still being suitable for standard machine learning hardware. The
model provides dynamic trade-offs between accuracy and energy use through feedback-
controlled precision gating and frequency-specific computatio
We outline the theoretical foundations of the NRA, including its governing equations, stability
proofs, and optimization functions. The architecture integrates resonance-based attention,
hierarchical modulation, and cross-modal coherence into one scalable structure that
supports visual, auditory, linguistic, and sensorimotor tasks. Extensive mathematical analysis
and conceptual simulations show that NRA networks can maintain stable adaptive learning
with significantly reduced energy costs while keeping task fidelity. Potential applicat
include neuromorphic hardware, robotics, multi-modal AI systems, and low-power embedded
devices.