Full Professor, University Göttingen
2 papers at NeurIPS 2025
TRACE is a contrastive learning framework that uses averaging of multi-trial neural activity to create interpretable 2D embeddings of large-scale neural recordings, revealing both continuous biological variation and discrete cell-type structures.
This paper presents a probabilistic model combining video inputs with stimulus-independent latent factors to model dynamic neural activity in mouse V1.
