# Gaussian Perceptual Field **Entity class:** Proposed representational model **Domain:** Computational Neuroscience / Neural Rendering / Perception **Doc Type:** Canonical Concept Node **Maturity:** Analytic synthesis **Primary source:** [[articles/The Closed-Loop Gaussian Sensorium Engine|The Closed-Loop Gaussian Sensorium Engine]] **Collection:** [[collections/Neurotech|Neurotech]] --- ## Definition A **Gaussian perceptual field** is the proposed machine-side state representation inside the [[wiki/Closed-Loop Gaussian Sensorium Engine|Closed-Loop Gaussian Sensorium Engine]]. It represents a subject's current and forecasted perceptual state as localized, uncertain, temporally persistent primitives rather than as a finished pixel image. ## Two-layer model 1. **Retinotopic field:** Gaussian-like kernels carry visual-field or cortical position, anisotropic extent, luminance or color, salience, persistence, and confidence. This layer is designed to align receptive-field maps, phosphene maps, and stimulation effects. 2. **Ventral-temporal axis field:** distributed object-feature axes carry higher-order content shared between perception and imagery. The 2026 Wadia et al. study is an empirical anchor for shared perception–imagery coding; it does not by itself establish a Gaussian implementation in cortex. The field is therefore a computational bridge between biological measurements and machine optimization. [[wiki/3D Gaussian Splatting|3D Gaussian Splatting]] supplies a useful renderer and differentiable primitive grammar, while the cortical analogy remains an article-level synthesis rather than a claim of literal identity. ## Relationships - **Maintained by:** [[wiki/Closed-Loop Gaussian Sensorium Engine|Closed-Loop Gaussian Sensorium Engine]]. - **Rendered through:** [[wiki/3D Gaussian Splatting|3D Gaussian Splatting]]. - **Updated from:** [[wiki/Neural Signal Acquisition|Neural Signal Acquisition]] and [[wiki/Neural Decoding|Neural Decoding]]. - **Used by:** [[wiki/Perceptual Attractor Seeding|Perceptual Attractor Seeding]] to select sparse candidate interventions. - **Interpreted through:** [[wiki/Predictive Processing|Predictive Processing]] and [[wiki/Active Inference|Active Inference]]. - **Governed by:** [[wiki/Perceptual Sovereignty|Perceptual Sovereignty]]. ## Evidence boundary Receptive-field models, phosphene simulators, neural decoding, and distributed object-axis codes are documented components. The combined two-layer field is a proposed integration. It must remain distinguishable from a measured neural object, a complete account of perception, or evidence that subjective experience has been reconstructed. ## Sources and provenance - [[articles/The Closed-Loop Gaussian Sensorium Engine|The Closed-Loop Gaussian Sensorium Engine]] — source of the two-layer field and its role in the Engine. - [Wadia et al., “A shared code for perceiving and imagining objects in human ventral temporal cortex” (Science, 2026)](https://pubmed.ncbi.nlm.nih.gov/41955351/). - [van der Grinten et al., differentiable phosphene simulation (eLife, 2024)](https://elifesciences.org/articles/85812). - [Kerbl et al., 3D Gaussian Splatting (SIGGRAPH 2023)](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/).