Over the last five decades, progress in neural recording techniques has allowed the number of simultaneously recorded neurons to double approximately every 7 years, mimicking Moore's law. Such exponential growth motivates us to ask how data analysis techniques are affected by progressively larger numbers of recorded neurons. Traditionally, neurons are analyzed independently on the basis of their tuning to stimuli or movement. Although tuning curve approaches are unaffected by growing numbers of simultaneously recorded neurons, newly developed techniques that analyze interactions between neurons become more accurate and more complex as the number of recorded neurons increases. Emerging data analysis techniques should consider both the computational costs and the potential for more accurate models associated with this exponential growth of the number of recorded neurons.
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Thanks to A. Kohn and members of the Kohn laboratory for providing data from visual cortex (US National Institutes of Health EY016774) and N. Hatsopoulos and J. Reimer for providing data from motor cortex. All animal use procedures were approved by the institutional animal care and use committees at Albert Einstein College of Medicine and the University of Chicago, respectively. Thanks to B. Yu and J. Cunningham for providing the GPFA code and B. Yu for insightful discussions. This work was supported by the Chicago Community Trust and US National Institutes of Health grants 1R01NS063399 and 2P01NS044393.
The authors declare no competing financial interests.
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Stevenson, I., Kording, K. How advances in neural recording affect data analysis. Nat Neurosci 14, 139–142 (2011). https://doi.org/10.1038/nn.2731
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