Receptive-field-constrained stimulus optimization for human early and intermediate visual cortex
Abstract
An ongoing challenge in sensory neuroscience is to characterize the feature dimensions encoded by cortical populations. Recent approaches probe feature selectivity in a data-driven way, by synthesizing a most-exciting-input (MEI) for a target neural population. While this approach has been successfully applied to human higher visual cortex using fMRI data, generating MEIs for early- and mid-level retinotopic visual areas requires additional modeling constraints due to small receptive field sizes. To address this challenge, we introduce two novel MEI generation frameworks, Receptive Field Diffusion for Visual Exploration (RF-DiVE) and Receptive Field Gradient Optimization (RF-GO). Both methods use a population receptive field (pRF)-constrained voxelwise encoding model; RF-DiVE combines this with a pretrained latent diffusion model, while RF-GO uses regularized gradient ascent. When applied to single voxels in retinotopically defined areas V1-hV4, using data from the Natural Scenes Dataset, we obtain MEIs that exhibit consistent structure within the pRF, suggesting selectivity for local features like contour, color, and texture. We systematically compare MEIs generated by RF-DiVE and RF-GO using two encoding backbones, performing in-silico validation of predicted responses to MEIs using independent encoding models. Across all methods and all visual areas, MEIs elicit higher model-predicted responses than the most activating natural images. We further find that the choice of generation framework and encoding backbone differentially affects MEI properties, including their visual appearance, structural interpretability, and cross-model generalizability. These results offer a new approach for performing data-driven characterization of spatial and feature selectivity across human visual cortex.
Related
- Rapid estimation of receptive fields across stages of the early visual system
- Dissociable Stimulus- and Response-Driven Serial Dependence Effects in Auditory, Visual, and Audiovisual Motion Judgments
- Attentional modulation of persistent visual representations after stimulus offset is coordinated across primary visual and anterior cingulate cortices
- Stimulus over expertise: what governs clinical visual attention in early health-vocational students.
- Progression of visual-stimulus-evoked blood velocity response timing across the vascular network measured with individual-vessel phase-contrast fMRA
BibTeX
@misc{zhao2026receptivefieldconstrained,
title = {{Receptive-field-constrained stimulus optimization for human early and intermediate visual cortex}},
author = {Junru Zhao and Hanfei Guo and Andrew Luo and Margaret M. Henderson},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.36391},
eprint = {2609.36391},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.36391},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Receptive-field-constrained stimulus optimization for human early and intermediate visual cortex
AU - Junru Zhao
AU - Hanfei Guo
AU - Andrew Luo
AU - Margaret M. Henderson
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.36391
UR - https://arxiv.org/abs/2609.36391
AB - An ongoing challenge in sensory neuroscience is to characterize the feature dimensions encoded by cortical populations. Recent approaches probe feature selectivity in a data-driven way, by synthesizing a most-exciting-input (MEI) for a target neural population. While this approach has been successfully applied to human higher visual cortex using fMRI data, generating MEIs for early- and mid-level retinotopic visual areas requires additional modeling constraints due to small receptive field sizes. To address this challenge, we introduce two novel MEI generation frameworks, Receptive Field Diffusion for Visual Exploration (RF-DiVE) and Receptive Field Gradient Optimization (RF-GO). Both methods use a population receptive field (pRF)-constrained voxelwise encoding model; RF-DiVE combines this with a pretrained latent diffusion model, while RF-GO uses regularized gradient ascent. When applied to single voxels in retinotopically defined areas V1-hV4, using data from the Natural Scenes Dataset, we obtain MEIs that exhibit consistent structure within the pRF, suggesting selectivity for local features like contour, color, and texture. We systematically compare MEIs generated by RF-DiVE and RF-GO using two encoding backbones, performing in-silico validation of predicted responses to MEIs using independent encoding models. Across all methods and all visual areas, MEIs elicit higher model-predicted responses than the most activating natural images. We further find that the choice of generation framework and encoding backbone differentially affects MEI properties, including their visual appearance, structural interpretability, and cross-model generalizability. These results offer a new approach for performing data-driven characterization of spatial and feature selectivity across human visual cortex.
ER -