A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision.

ABSTRACT: Large-scale visual neural datasets such as the Natural Scenes Dataset (NSD) are boosting computational neuroscience research by enabling models of the brain with performances beyond what was possible just a decade ago. However, because the stimuli of these datasets typically live within a common naturalistic visual distribution, they do not allow for strict out-of-distribution (OOD) generalization tests which are crucial for the development of more robust models. Here, we address this limitation by releasing NSD-synthetic, a dataset consisting of 7T fMRI responses from the same eight NSD participants for 284 synthetic images. We show that NSD-synthetic's fMRI responses reliably encode stimulus-related information and are OOD with respect to NSD. Furthermore, we provide a proof of principle

SUBMITTER: Gifford AT 

PROVIDER: S-EPMC12440068 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature