Ken Takeda

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

This paper introduces an unsupervised alignment method using Gromov-Wasserstein Optimal Transport to compare neural representations of natural scenes across individuals and brain areas, finding alignment within visual cortical areas but not across different areas.

Unsupervised Alignment in Neuroscience: Introducing a Toolbox for Gromov-Wasserstein Optimal Transport

This paper introduces an unsupervised alignment method using Gromov-Wasserstein optimal transport for neuroscience, showing its ability to reveal structural distinctions in representations across different data domains.