In vivo cell-type and brain region classification via multimodal contrastive learning

Han Yu, Hanrui Lyu, YiXun Xu, Charlie Windolf, Eric Kenji Lee, Fan Yang, Andrew M Shelton, Olivier Winter, International Brain Laboratory, Eva L Dyer, Chandramouli Chandrasekaran, Nicholas A. Steinmetz, Liam Paninski, Cole Lincoln Hurwitz

Keywords: contrastive learning, electrophysiology, extracellular, multimodal, neuroscience, cell type, brain region, Neuropixels, deep learning

Abstract: Current electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded without 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 multimodal contrastive learning 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 two opto-tagged datasets 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.

Primary Area: applications to neuroscience & cognitive science

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Submission Number: 13258