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Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of neurons, requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate (the number of neurons synchronously active) and the probability that each individual neuron fires given the population rate. We found that this model can accurately fit synthetic data from up to 1000 neurons. We also found that the model could rapidly decode visual stimuli from neural population data from macaque primary visual cortex about 65\u00a0ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse somatosensory cortex and, surprisingly, found that it first increases, and then decreases during development. This statistical model opens new options for interrogating neural population data and can bolster the use of modern large-scale in vivo Ca[Formula: see text] and voltage imaging tools.<\/jats:p>","DOI":"10.1162\/neco_a_00910","type":"journal-article","created":{"date-parts":[[2016,11,21]],"date-time":"2016-11-21T15:46:09Z","timestamp":1479743169000},"page":"50-93","source":"Crossref","is-referenced-by-count":14,"title":["The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data"],"prefix":"10.1162","volume":"29","author":[{"given":"Cian","family":"O\u2019Donnell","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Bristol, Bristol BS81UB. U.K., and Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. Tiago","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A., and Departments of Neurology and Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nick","family":"Whiteley","sequence":"additional","affiliation":[{"name":"School of Mathematics, University of Bristol, Bristol BS81UB, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos","family":"Portera-Cailliau","sequence":"additional","affiliation":[{"name":"Departments of Neurology and Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Terrence J.","family":"Sejnowski","sequence":"additional","affiliation":[{"name":"Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A., and Division of Biological Sciences, University of California at San Diego, La Jolla, CA 92093, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1162\/089976603321043720"},{"key":"B2","first-page":"1700","volume-title":"Advances in neural information processing systems","volume":"26","author":"Archer E. 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