Center for Neural Data Science Seminar with Yiqi Jiang

Talk Title: Extracting task-relevant preserved dynamics from contrastive aligned neural recordings

Speaker Bio: 

Yiqi is broadly interested in motor control and learning, and how to leverage machine learning and AI to model the brain using large scale datasets. She completed her undergraduate at Cornell University. She is currently a 4th year PhD student at the Electrical Engineering Department advised by Dr. Mark Schnitzer and Dr. Scott Linderman. She is a member of the MBCT program and the recipient of the Stanford Shenoy-Simons Foundation Grant in 2025.

Abstract:

Recent work indicates that low-dimensional dynamics of neural and behavioral data are often preserved across days and subjects. However, extracting these preserved dynamics remains challenging: high-dimensional neural population activity and the recorded neuron populations vary across recording sessions. While existing modeling tools can improve alignment between neural and behavioral data, they often operate on a per-subject basis or discretize behavior into categories, disrupting its natural continuity and failing to capture the underlying dynamics. We introduce Contrastive Aligned Neural DYnamics (CANDY), an end‑to‑end framework that aligns neural and behavioral data using rank-based contrastive learning, adapted for continuous behavioral variables, to project neural activity from different sessions onto a shared low-dimensional embedding space. Our results show that CANDY is able to learn aligned latent embeddings and preserved dynamics across neural recording sessions and subjects, and it achieves improved cross-session behavior decoding performance. These advances enable robust cross‑session behavioral decoding and offer a path towards identifying shared neural dynamics that underlie behavior across individuals and recording conditions.

 Happy Hour/Reception for Attendees

Seminars attendees are invited to stay for a happy hour reception after the seminar to continue the conversation with the speaker.

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