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Dimensionality reduction for large-scale neural recordings

Nature Neuroscience volume 17, pages 1500–1509 (2014)Cite this article

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Abstract

Most sensory, cognitive and motor functions depend on the interactions of many neurons. In recent years, there has been rapid development and increasing use of technologies for recording from large numbers of neurons, either sequentially or simultaneously. A key question is what scientific insight can be gained by studying a population of recorded neurons beyond studying each neuron individually. Here, we examine three important motivations for population studies: single-trial hypotheses requiring statistical power, hypotheses of population response structure and exploratory analyses of large data sets. Many recent studies have adopted dimensionality reduction to analyze these populations and to find features that are not apparent at the level of individual neurons. We describe the dimensionality reduction methods commonly applied to population activity and offer practical advice about selecting methods and interpreting their outputs. This review is intended for experimental and computational researchers who seek to understand the role dimensionality reduction has had and can have in systems neuroscience, and who seek to apply these methods to their own data.

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Figure 1: Motivation for population analyses and dimensionality reduction.
Figure 2: Conceptual illustration of linear dimensionality reduction for three neurons (D = 3) and two latent variables (K = 2).
Figure 3: Examples of scientific studies using dimensionality reduction.
Figure 4: Conceptual illustration of PCA, LDA and demixed dimensionality reduction for two neurons (D = 2).

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Acknowledgements

We are grateful to the authors and publishers of the works highlighted in Figure 3 for permission to reuse portions of their figures. We thank C. Chandrasekaran, A. Miri, W. Newsome, B. Raman, and the members of the laboratories of A. Batista, S. Chase and M. Churchland for helpful discussion during the preparation of this manuscript. This work was supported by the Grossman Center for the Statistics of Mind (J.P.C.), the Simons Foundation (SCGB-325171 and SCGB-325233 to J.P.C.), the Gatsby Charitable Foundation (J.P.C.) and the US National Institutes of Health National Institute of Child Health and Human Development (R01-HD-071686 to B.M.Y.).

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  1. Department of Statistics, Columbia University, New York, New York, USA

    John P Cunningham

  2. Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Byron M Yu

  3. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Byron M Yu

  4. Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Byron M Yu

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Cunningham, J., Yu, B. Dimensionality reduction for large-scale neural recordings. Nat Neurosci 17, 1500–1509 (2014). https://doi.org/10.1038/nn.3776

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