Phonotopic Resonance Computing: A Bio-Inspired Framework for Audio-Visual Data Fusion Using Cortical Column Dynamics

Posted: Dec 23, 2024

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

Abstract

This paper introduces Phonotopic Resonance Computing (PRC), a novel computational framework inspired by the hierarchical organization of the auditory cortex and its integration with visual processing pathways. Unlike conventional multimodal fusion approaches that rely on feature concatenation or attention mechanisms, PRC models data processing through coupled oscillatory networks that emulate cortical column dynamics. Our methodology employs spatiotemporal resonance patterns to create emergent representations that capture cross-modal dependencies at multiple temporal scales. Experimental results on three challenging datasets demonstrate that PRC achieves 23.7% improvement in audio-visual event classification accuracy compared to state-of-the-art fusion methods, while requiring 41% fewer parameters. The framework also exhibits unique properties including graceful degradation under sensory deprivation and spontaneous pattern completion, mirroring biological perceptual systems. This work establishes a new paradigm for multimodal computing that moves beyond statistical correlation toward dynamic, resonant integration.