• Corpus ID: 46346135

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

@inproceedings{Nonnenmacher2017ExtractingLD,
  title={Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations},
  author={Marcel Nonnenmacher and Srinivas C. Turaga and Jakob H. Macke},
  booktitle={NIPS},
  year={2017}
}
A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedded across multiple measurements. We propose an approach for extracting low-dimensional dynamics from multiple, sequential recordings. Our algorithm scales to data comprising… 

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