Research map / Vision-language and multimodal / Human and embodied modeling
In vivo cell-type and brain region classification via multimodal contrastive learning
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Proposes a multimodal contrastive learning method that classifies cell-type and brain region of in vivo recorded neurons from electrophysiological data.
Problem
Electrophysiological recordings track many neurons, but the cell-type and brain region of recorded neurons are usually unknown without molecular or histological analysis.
Approach
A multimodal contrastive learning approach that jointly relates electrophysiological signals to cell-type and brain-region labels to enable accurate, scalable identification of recorded neurons.
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