Kavli Affiliate: Liam Paninski
| Authors: Han Yu, Hanrui Lyu, Ethan YiXun Xu, Charlie Windolf, Eric Kenji Lee, Fan Yang, Andrew M Shelton, Shawn Olsen, Sahar Minavi, Olivier Winter, The International Brain Laboratory, Eva L Dyer, Chandramouli Chandrasekaran, Nicholas A Steinmetz, Liam Paninski and Cole Hurwitz
| Summary:
Current electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded with-out further molecular or histological analysis. Developing accurate and scalable algorithms for identifying the cell-type and brain region of recorded neurons is thus crucial for improving our understanding of neural computation. In this work, we develop a multimodal contrastive learning approach for neural data that can be fine-tuned for different downstream tasks, including inference of cell-type and brain location. We utilize this approach to jointly embed the activity autocorrelations and extracellular waveforms of individual neurons. We demonstrate that our embedding approach, Neuronal Embeddings via MultimOdal contrastive learning (NEMO), paired with supervised fine-tuning, achieves state-of-the-art cell-type classification for an opto-tagged visual cortex dataset and brain region classification for the public International Brain Laboratory brain-wide map dataset. Our method represents a promising step towards accurate cell-type and brain region classification from electrophysiological recordings.