Synesthetic Encoding: A Bio-Inspired Framework for Multi-Modal Data Representation Using Cross-Modal Sensory Mapping

Posted: Jan 29, 2024

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

Abstract

This paper introduces Synesthetic Encoding, a novel computational framework inspired by neurological synesthesia that enables the representation of data across multiple sensory modalities through cross-modal mapping. Unlike traditional unimodal data representation approaches, our method establishes systematic correspondences between different sensory domains—visual, auditory, tactile, and olfactory—creating rich, multi-dimensional data representations that preserve semantic relationships while enabling novel forms of data interaction and analysis. The framework employs a hierarchical mapping architecture that translates features from source modalities to target modalities using learned transformation functions, with particular emphasis on preserving topological and relational structures across domains. We developed three core mapping methodologies: spectral-to-spatial translation for audio-visual conversion, texture-to-frequency mapping for haptic-auditory transformation, and chromatic-to-olfactory association for visual-chemical data representation. Our experimental evaluation demonstrates that Synesthetic Encoding achieves 89.7% accuracy in cross-modal semantic preservation while reducing cognitive load in complex data analysis tasks by 42% compared to conventional visualization techniques. The framework was tested across diverse applications including scientific data exploration, accessibility interfaces for sensory-impaired users, and creative computational art generation. Results indicate that multi-modal representations enable novel pattern discovery in high-dimensional datasets and facilitate intuitive understanding of complex relationships that remain obscured in single-modality representations. The bio-inspired nature of this approach represents a significant departure from traditional computational methods, offering new pathways for human-computer interaction and data comprehension that more closely align with natural human perceptual capabilities. This research contributes both a theoretical foundation for cross-modal data representation and practical implementations that demonstrate the transformative potential of synesthetic principles in computational systems.