RT Journal Article
SR Electronic
T1 Random versus maximum entropy models of neural population activity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 092973
DO 10.1101/092973
A1 Ulisse Ferrari
A1 Tomoyuki Obuchi
A1 Thierry Mora
YR 2016
UL http://biorxiv.org/content/early/2016/12/09/092973.abstract
AB The principle of maximum entropy provides a useful method for inferring statistical mechanics models from observations in correlated systems, and is widely used in a variety of fields where accurate data are available. While the assumptions underlying maximum entropy are intuitive and appealing, its adequacy for describing complex empirical data has been little studied in comparison to alternative approaches. Here data from the collective spiking activity of retinal neurons is reanalysed. The accuracy of the maximum entropy distribution constrained by mean firing rates and pairwise correlations is compared to a random ensemble of distributions constrained by the same observables. In general, maximum entropy approximates the true distribution better than the typical or mean distribution from that ensemble. This advantage improves with population size, with groups as small as 8 being almost always better described by maximum entropy. Failure of maximum entropy to outperform random models is found to be associated with strong correlations in the population.