These results fall under the category 'the unreasonable effectiveness of linear methods'. Nonlinear methods are sometimes essential to progress: we love and use AEN, GAN, LFADS, and others. But sometimes a good understanding and use of linear methods is all that is needed. 2/n
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To me is still amazing that linear approaches suffice to align complex low-dimensional dynamics across days, months, even years! Not only the results are exciting, but we learned much in the process. 3/n
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We are grateful to our Nature Neuroscience editor, who guided us and supported us through a long and difficult review process: three revisions over one full year. Some colleagues are very attached to the idea of a simple one-dimensional tuning curve to each neuron. 4/n
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The problem resides with the belief that individual tuning curves can explain any and all observed neural activity, and with the refusal to accept the need for population analyses. So, to our most difficult and recalcitrant anonymous reviewer: I hope you learned something. 5/n
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And to all those who cling to their most dearest beliefs to the point of hindering progress, I offer the words of a Nobel Laureate:pic.twitter.com/7sANAGvuHh
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Sara, having read the bioRxiv version that appeared Nov 2018, I believe this very interesting work.
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Thanks, Saleh!
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Fantastic news! Congrats!!
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Thanks, Savio!
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Excited to hear about this and read the paper tonight. We see large turnover in active ensemble membership in mouse S1 during discrimination training, but high stability in tuning at population distribution and persistent individual neuron level. More soon...
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Thanks, Andrew. Looking forward to your comments!
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