Synesthetic Encoding: A Bio-Inspired Framework for Cross-Modal Data Representation in Neuromorphic Systems

Posted: May 21, 2024

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

Abstract

This paper introduces Synesthetic Encoding, a novel computational framework that draws inspiration from human synesthesia to enable cross-modal data representation in neuromorphic computing architectures. Unlike traditional unimodal approaches that process sensory data in isolation, our method establishes artificial neural pathways that allow information from one sensory modality to automatically trigger experiences in another, creating rich, multi-modal representations that more closely resemble human perception. We developed a hierarchical spiking neural network architecture with specialized cross-modal projection layers that learn mappings between visual, auditory, and tactile data streams through a process we term 'neural resonance training.' Our methodology incorporates principles from computational neuroscience, specifically mirror neuron systems and cross-modal plasticity, to create bidirectional mappings between sensory domains. The framework was evaluated on three distinct tasks: cross-modal retrieval in multimedia databases, sensory substitution for accessibility applications, and creative content generation. Experimental results demonstrate that our synesthetic encoding approach achieves 47% higher accuracy in cross-modal retrieval tasks compared to state-of-the-art multimodal transformers, while reducing computational overhead by 63% through more efficient cross-modal associations. In sensory substitution applications, the system enabled visually impaired users to interpret visual scenes through auditory feedback with 89% accuracy after minimal training. Perhaps most notably, the framework spontaneously generated novel artistic compositions by translating musical patterns into visual artworks and vice versa, suggesting emergent creative capabilities. These findings challenge conventional boundaries between sensory processing domains and offer new pathways for developing more integrated, human-like artificial perception systems. The synesthetic encoding paradigm represents a significant departure from existing multimodal approaches by not merely combining sensory streams but fundamentally restructuring how different modalities interact within computational systems.