HyNeuralMap: Hyperbolic Mapping of Visual Semantics to Neural Hierarchies
Zihan Ma, Tian Xia, Kexin Wang, Xiao Li, Xiaowei He, Yudan Ren

TL;DR
HyNeuralMap introduces a hyperbolic geometric framework to better align visual semantics with neural responses, capturing hierarchical structures more effectively than traditional Euclidean methods.
Contribution
The paper presents a novel hyperbolic Lorentz model for mapping visual semantics to neural hierarchies, improving semantic preservation and cross-subject neural similarity modeling.
Findings
Outperforms Euclidean baselines in semantic prediction tasks.
Effectively captures hierarchical semantic relationships.
Enhances cross-modal retrieval accuracy.
Abstract
Understanding the intricate mappings between visual stimuli and neural responses is a fundamental challenge in cognitive neuroscience. While current approaches predominantly align images and functional magnetic resonance imaging (fMRI) responses in Euclidean space, this geometry often struggles to preserve fine-grained semantic relationships and latent hierarchical structures across visual and neural modalities. To overcome this, we propose HyNeuralMap, a framework that employ hyperbolic Lorentz model to map visual semantics into a shared, cross-subject neural hierarchy. By leveraging the negative curvature of hyperbolic space as an inductive bias, the proposed framework better captures hierarchical semantic organization and cross-subject neural similarities. Specifically, visual and neural embeddings are jointly optimized through hyperbolic geometric alignment, where geodesic distances…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
