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

Nonnenmacher, M.; Turaga, S.C.; Macke, Jakob H.

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

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Macke, Jakob H.
Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Max Planck Society;

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https://papers.nips.cc/paper/7153-extracting-low-dimensional-dynamics-from-multiple-large-scale-neural-population-recordings-by-learning-to-predict-correlations
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Nonnenmacher, M., Turaga, S., & Macke, J. H. (2018). Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations. In Advances in Neural Information Processing Systems 30 (NIPS 2017).

Cite as: https://hdl.handle.net/21.11116/0000-0000-24E8-2

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