3D Spatially-Embedded Multimodal Manifold Network (3D-SEMMN)

A Theoretical Proposal bridging brain-like 3D geometry, neural manifolds, and multimodal integration.

Paper PDF: Download here
GitHub Code: https://github.com/MarcusRich/3D-SEMMN
arXiv Preprint (coming soon)

Abstract

Biological brains achieve robust multimodal perception and generalization through explicit 3D spatial organization: neurons cluster into modular columns, share hardened white-matter pathways, and collapse high-dimensional activity onto low-dimensional manifolds. Current multimodal AI (e.g., CLIP, ImageBind) performs abstract fusion in latent space but ignores these physical constraints, leading to high parameter counts, energy inefficiency, and limited cross-modal imagination. We propose the **3D Spatially-Embedded Multimodal Manifold Network (3D-SEMMN)**—a novel architecture that embeds spiking or rate-based neurons in a simulated 3D Euclidean grid, routes modality-specific inputs to segregated cortical-like lobes, enforces distance-penalized sparse long-range connections, and regularizes population dynamics onto learnable low-dimensional manifolds. Inspired by seRNNs (which spontaneously form cortical-like modularity), cortical-column voting (Thousand Brains Theory), multisensory spiking networks (MSeNN), and observed sensory-to-perceptual manifold twists, 3D-SEMMN enables pathway hardening via Hebbian plasticity, cross-modal fusion in a shared manifold hub, and hardware mapping to 3D-stacked neuromorphic chips. This theoretical framework outlines the full mathematical formulation, training protocol, and proposed experiments that we predict will demonstrate superior efficiency, generalization, and imagination on multimodal benchmarks. This framework closes the gap between biological 3D efficiency and artificial intelligence.

Keywords: Neural manifolds, spatial embedding, multimodal integration, neuromorphic hardware, cortical columns

Seeking arXiv Endorsement

I am an independent researcher seeking one cs.AI endorsement to finalize the arXiv submission. If you are an established arXiv author in cs.AI (≥3 prior papers), I would greatly appreciate your help!

Endorsement request link: https://arxiv.org/auth/endorse?x=B3lC3T

Full code, visualizations, and ablation studies are included.