The critical brain hypothesis has emerged in the last decades as a fruitful theoretical framework for understanding collective neuronal phenomena. Lending support to the idea that the brain operates near a phase transition, Beggs and Plenz were the first to report experimentally recorded neuronal avalanches, whose distributions coincide with the mean-field directed percolation (DP) universality class, which comprises a variety of models in which a phase transition occurs between an absorbing (silent) and an active phase.
However, this hypothesis is highly debated, as neuronal avalanches analyses and other common statistical mechanics tools may struggle with challenges ubiquitous in living systems, such as subsampling, long range correlations and the absence of an explicit model for the complete neuronal dynamics. In this context, Meshulam et al. recently proposed a phenomenological renormalization group (PRG) method to deal with neural networks typical long range interactions with a model independent analysis.
The procedure consists of repeatedly manipulating the data, obtaining a increasingly coarse-grained description of the activity after each iteration. Under a critical regime, non-trivial correlations and scale-free behavior should be unveiled as we simplify our description. This can be inferred from a series of statistical features of the data, which lead us to different scaling relations.
Here, we apply this phenomenological renormalization group (PRG) in different experimental setups. Additionally, we investigate how the scaling exponents found via PRG behave as we parse our data by its coefficient of variation (CV); this measurement has appeared in recent literature as a means of tracking different cortical states through spiking variability.