  Large-scale recordings of neuronal activity make it possible to gain insights
into the collective activity of neural ensembles. It has been hypothesized that
neural populations might be optimized to operate at a 'thermodynamic critical
point', and that this property has implications for information processing.
Support for this notion has come from a series of studies which identified
statistical signatures of criticality in the ensemble activity of retinal
ganglion cells. What are the underlying mechanisms that give rise to these
observations? Here we show that signatures of criticality arise even in simple
feed-forward models of retinal population activity. In particular, they occur
whenever neural population data exhibits correlations, and is randomly
sub-sampled during data analysis. These results show that signatures of
criticality are not necessarily indicative of an optimized coding strategy, and
challenge the utility of analysis approaches based on equilibrium
thermodynamics for understanding partially observed biological systems.
