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

Posted: Oct 28, 2025

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

Abstract

The human brain possesses remarkable capabilities for integrating information across multiple sensory modalities, a phenomenon most dramatically demonstrated in synesthesia—a neurological condition where stimulation of one sensory pathway leads to automatic experiences in a second sensory pathway. While computational systems have traditionally operated within isolated sensory domains, we propose that embracing cross-modal representation can unlock new possibilities for data comprehension, analysis, and interaction. Current data representation methodologies suffer from modality-specific limitations: visualizations can become cluttered with high-dimensional data, auditory representations often lack precision, and haptic interfaces remain underdeveloped for complex data. Our research addresses these limitations by developing a systematic framework for cross-modal data representation that establishes meaningful correspondences between sensory domains. The core contribution of this work is Synesthetic Encoding, a bio-inspired computational framework that enables seamless translation of data characteristics across visual, auditory, tactile, and olfactory modalities. Unlike previous multi-modal approaches that simply present the same information through different channels, our method creates integrated representations where modalities complement and enhance each other through carefully designed mapping functions. We investigate three primary research questions: (1) How can we establish mathematically rigorous mappings between fundamentally different sensory domains? (2) What principles ensure that semantic relationships are preserved across modality transformations? (3) What practical benefits do cross-modal representations offer for data analysis and comprehension tasks?