Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations

Marcel Nonnenmacher, Srinivas C. Turaga, Jakob H. Macke

Nov-21-2025, 12:51:58 GMT–Neural Information Processing Systems

Our algorithm scales to data comprising millions of observed dimensions, making it possible to access dynamics distributed across large populations or multiple brain areas.

artificial intelligence, covariance, machine learning, (17 more...)

Neural Information Processing Systems

Nov-21-2025, 12:51:58 GMT

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Country:
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  - Hesse > Darmstadt Region
    - Darmstadt (0.04)
  - North Rhine-Westphalia > Cologne Region
    - Bonn (0.04)
- North America > United States
  - California > Los Angeles County > Long Beach (0.04)

Industry:
- Health & Medicine > Therapeutic Area > Neurology (0.46)

Technology:
- Information Technology > Artificial Intelligence
  - Machine Learning > Statistical Learning (1.00)
  - Representation & Reasoning (0.69)

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Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations

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