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Large-scale recordings of neuronal activity make it possible to gain insightsinto the collective activity of neural ensembles. It has been hypothesized thatneural populations might be optimized to operate at a 'thermodynamic criticalpoint', and that this property has implications for information processing.Support for this notion has come from a series of studies which identifiedstatistical signatures of criticality in the ensemble activity of retinalganglion cells. What are the underlying mechanisms that give rise to theseobservations? Here we show that signatures of criticality arise even in simplefeed-forward models of retinal population activity. In particular, they occurwhenever neural population data exhibits correlations, and is randomlysub-sampled during data analysis. These results show that signatures ofcriticality are not necessarily indicative of an optimized coding strategy, andchallenge the utility of analysis approaches based on equilibriumthermodynamics for understanding partially observed biological systems.

Signatures of criticality arise in simple neural population models with correlations