Subsampled directed-percolation models explain scaling relations experimentally observed in the brain

Abstract

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.

15 pages, 9 figures, submitted to Frontiers Neural Circuits

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• Sampling effects and measurement overlap can bias the inference of neuronal avalanches
• Building a model of the brain: from detailed connectivity maps to network organization
• Emergence of order from chaos through a continuous phase transition in a turbulent reactive flow system
• Modelling on the very large-scale connectome
• Stochastic quorum percolation and noise focusing in neuronal networks
• Optimal input reverberation and homeostatic self-organization towards the edge of synchronization
• Less is different: why sparse networks with inhibition differ from complete graphs