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  • title: Towards robust and generalizable representations of extracellular data using contrastive learning
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            Towards robust and generalizable representations of extracellular data using contrastive learning
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            Towards robust and generalizable representations of extracellular data using contrastive learning

            Dec 10, 2023

            Speakers

            AV

            Ankit Vishnubhotla

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            CL

            Charlotte Loh

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            LP

            Liam Paninski

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            About

            Contrastive learning is quickly becoming an essential tool in neuroscience for extracting robust and meaningful representations of neural activity. Despite numerous applications to neuronal population data, there has been little exploration of how these methods can be adapted to key primary data analysis tasks such as spike sorting or cell-type classification. In this work, we propose a novel contrastive learning framework, CEED (Contrastive Embeddings for Extracellular Data), for high-density …

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            NeurIPS 2023

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