The population tracking model: a simple, scalable statistical model for neural population data

Cian O'Donnell, J Tiago Goncalves, Nick P Whiteley, Carlos Portera-Cailliau, Terrence J Sejnowski

Research output: Contribution to journalArticle (Academic Journal)peer-review

11 Citations (Scopus)
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Abstract

Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded (~ 2Neurons). 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, so 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, ~ 65 ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse matosensory cortex and surprisingly found that it first increases, 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 Ca2+ and voltage imaging tools.
Original languageEnglish
Pages (from-to)50-93
Number of pages44
JournalNeural Computation
Volume29
Issue number1
Early online date29 Dec 2016
DOIs
Publication statusPublished - 1 Jan 2017

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