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Recent experimental results on spike avalanches measured in the urethane-anesthetized rat cortex have revealed scaling relations that indicate a phase transition at a specific level of cortical firing rate variability. The scaling relations point to critical exponents whose values differ from those of a branching process, which has been the canonical model employed to understand brain criticality. This suggested that a different model, with a different phase transition, might be required to explain the data. Here we show that this is not necessarily the case. By employing two different models belonging to the same universality class as the branching process (mean-field directed percolation) and treating the simulation data exactly like experimental data, we reproduce most of the experimental results. We find that subsampling the model and adjusting the time bin used to define avalanches (as done with experimental data) are sufficient ingredients to change the apparent exponents of the critical point. Moreover, experimental data is only reproduced within a very narrow range in parameter space around the phase transition., Comment: 15 pages, 9 figures, submitted to Frontiers Neural Circuits

Since the first measurements of neuronal avalanches, the critical brain hypothesis has gained traction. However, if the brain is critical, what is the phase transition? For several decades, it has been known that the cerebral cortex operates in a diversity of regimes, ranging from highly synchronous states (with higher spiking variability) to desynchronized states (with lower spiking variability). Here, using both new and publicly available data, we test independent signatures of criticality and show that a phase transition occurs in an intermediate value of spiking variability, in both anesthetized and freely moving animals. The critical exponents point to a universality class different from mean-field directed percolation. Importantly, as the cortex hovers around this critical point, the avalanche exponents follow a linear relation that encompasses previous experimental results from different setups and is reproduced by a model., NPAD/UFRN. A. J. F., N. A. P. V., T. F., L. A. A. A,L. D. P., S. R., P. V. C., and M. C. acknowledge supportfrom CAPES (Grants No. 88887.131435/2016-00 andPROEX 534/2018, No. 23038.003382/2018-39),FACEPE (Grant No. APQ 0826-1.05/15) and CNPq(Grants No. 310712/2014-9, No. 301744/2018-1,No. 425329/2018-6, No. 308775/2015-5, No. 249991/2013-6, and No. 408145/2016-1). This article was pro-duced as part of the activities of FAPESP Research,InnovationandDisseminationCenterforNeuromathematics (Grant No. 2013/07699-0, S. PauloResearch Foundation). C. S.-C. and B. C. acknowledgesupport from FCT (Grants No. SFRH/BD/51992/2012and No. SFRH/BD/98675/2013) and PAC-MEDPERSYST Project No. POCI-01-0145-FEDER-016428 (Portugal 2020); A. J. R. received support from anFCT Investigator Fellowship (IF/00883/2013) and acknowl-edges the Janssen Neuroscience Prize (first edition) and theBIAL Foundation Grant No. 30/2016