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

Posted: Oct 28, 2025

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

Abstract

The human brain possesses remarkable capabilities for integrating information across sensory modalities, a phenomenon most dramatically illustrated in synesthesia, where stimulation of one sensory pathway leads to automatic experiences in another. While artificial intelligence has made significant strides in processing individual sensory modalities, current systems largely lack the rich cross-modal integration that characterizes human perception. This paper introduces Synesthetic Encoding, a computational framework that bridges this gap by implementing bio-inspired cross-modal associations in neuromorphic architectures. Traditional multimodal approaches typically process sensory streams in parallel and combine them at later stages, missing the fundamental integration that occurs in biological systems. Our work is distinguished by its direct modeling of cross-modal neural pathways, creating a system where representations in one modality inherently contain information about other modalities. This approach represents a paradigm shift from conventional multimodal processing toward truly integrated sensory representation.