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 (~ 2^{Neurons}). Here we introduce a new statistical method for characterizing neural population activity that requires semiindependent 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 largescale in vivo Ca^{2+} and voltage imaging tools.
Original language  English 

Pages (fromto)  5093 
Number of pages  44 
Journal  Neural Computation 
Volume  29 
Issue number  1 
Early online date  29 Dec 2016 
DOIs  
Publication status  Published  1 Jan 2017 
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Profiles

Dr Cian O'Donnell
 Department of Computer Science  Senior Lecturer in Computer Science
 Bristol Neuroscience
Person: Academic , Member