Signatures of criticality arise in simple neural population models with correlations

TL;DR

This paper demonstrates that signatures of criticality in neural population activity can emerge from simple models with correlations and random sampling, questioning their interpretation as optimized coding.

Contribution

It shows that criticality signatures can arise in basic neural models with correlations, challenging their use as indicators of optimal information processing.

Findings

Criticality signatures appear in simple models with correlations.

Sampling methods influence the detection of criticality.

Signatures are not necessarily linked to optimized coding strategies.

Abstract

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.