Unsupervised alignment reveals structural commonalities and differences in neural representations of natural scenes across individuals and brain areas.

Neuroscience research aims to identify universal neural mechanisms underlying sensory information encoding by comparing neural representations across individuals, typically using Representational Similarity Analysis. However, traditional methods assume direct stimulus correspondence across individuals, limiting the exploration of other possibilities. To address this, we propose an unsupervised alignment framework based on Gromov-Wasserstein Optimal Transport, which identifies correspondences between neural representations solely from internal similarity structures, without relying on stimulus labels. Applying this method to Neuropixels recordings in mice and fMRI data in humans viewing natural scenes, we found that the neural representations in the same visual cortical areas can be well aligned across individuals in an unsupervised manner. Furthermore, alignment across different brain areas is influenced by factors beyond the visual hierarchy, with higher-order visual areas aligning well with each other, but not with lower-order areas. This unsupervised approach reveals more nuanced structural commonalities and differences in neural representations than conventional methods.