Synaptic Resonance Computing: A Bio-Inspired Framework for Dynamic Information Processing in Unstable Environments

Posted: Dec 19, 2024

• Dr. Elara Vance
• Prof. Kenji Tanaka
• Dr. Isabella Rossi

Abstract

Traditional computing paradigms struggle with dynamic, noisy environments where information reliability fluctuates unpredictably. This paper introduces Synaptic Resonance Computing (SRC), a novel computational framework inspired by the dynamic filtering properties of biological neural systems. Unlike conventional approaches that treat noise as a problem to be eliminated, SRC leverages temporal information patterns to distinguish meaningful signals from irrelevant fluctuations through resonance-based processing. Our methodology integrates concepts from computational neuroscience, dynamical systems theory, and information geometry to create adaptive computational elements that selectively amplify coherent information while suppressing chaotic noise. The core innovation lies in the resonance modulation mechanism, which enables computational units to dynamically adjust their sensitivity to input patterns based on temporal coherence rather than amplitude thresholds. We implemented SRC in both software simulation and a custom FPGA architecture, testing its performance across three challenging domains: real-time financial market analysis with high-frequency noise, autonomous navigation in visually degraded environments, and medical signal processing from noisy physiological sensors. Results demonstrate that SRC achieves 47% higher accuracy in signal classification under high-noise conditions compared to traditional neural networks, while reducing computational overhead by 32% through selective processing. The framework exhibits emergent properties including graceful degradation under increasing noise levels and adaptive learning without explicit retraining. These findings suggest that resonance-based computation represents a fundamentally different approach to information processing that more closely mirrors biological intelligence's robustness in uncertain environments. The theoretical contributions include a mathematical formulation of synaptic resonance dynamics and practical implementations that bridge neuroscience principles with computational engineering.