[CPCN Seminar]
Speaker
Prof. Ambuj SinghLocation
Psych 1312Info
Recent progress in brain reconstruction has shown that visual and semantic information can be decoded from fMRI into high-level representation spaces. In this talk, we outline a broader research direction centered on representation alignment as a unifying principle for brain modeling. First, we discuss ongoing efforts to construct a shared, aligned brain representation space that maps multiple subjects into a common geometry. Rather than treating each subject or dataset independently, this framework aims to formalize subject-agnostic alignment and improve data efficiency through structured adapters.
Second, we describe extensions beyond fMRI to M/EEG, exploring whether heterogeneous modalities can be integrated into the same representational space despite differences in spatial and temporal resolution. This raises foundational questions about what aspects of neural geometry are modality-invariant.
Finally, we broaden the perspective to task-dependent representations and comparisons with modern AI systems, including vision and language models. By studying representation structure, alignment, and geometry across biological and artificial systems, we aim to better understand how task demands shape internal spaces and where human and machine representations diverge.
