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

Posted: Oct 28, 2025

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

Abstract

Contemporary computing systems face increasing challenges in processing information from dynamic, noisy environments where signal reliability fluctuates unpredictably. Traditional approaches, including conventional neural networks and statistical filtering methods, typically treat noise as an undesirable artifact to be removed or suppressed. However, biological neural systems demonstrate remarkable robustness in similar conditions, suggesting alternative computational principles may exist. This paper introduces Synaptic Resonance Computing (SRC), a novel framework that fundamentally rethinks how computational systems process information in unstable environments. SRC draws inspiration from recent neuroscientific discoveries regarding synaptic filtering and resonance phenomena in neural circuits. Rather than attempting to eliminate noise, SRC employs dynamic resonance mechanisms to selectively amplify temporally coherent information patterns while suppressing chaotic fluctuations. This approach represents a paradigm shift from amplitude-based to coherence-based information processing. Our research addresses three key questions: (1) How can computational systems leverage temporal coherence rather than signal amplitude for information discrimination? (2) What mathematical principles govern resonance-based information processing? (3) Can such systems demonstrate practical advantages