Departamento de Física, Universidade Federal de Pernambuco, Recife, Brazil.
Department of Physics, University of Ottawa, Ottawa, ON, Canada.
Front Neural Circuits. 2021 Jan 15;14:576727. doi: 10.3389/fncir.2020.576727. eCollection 2020.
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.
最近在麻醉大鼠皮层中测量尖峰爆发的实验结果揭示了标度关系，表明在皮层发射率可变性的特定水平存在相变。这些标度关系指向临界指数，其值与分支过程的临界指数不同，分支过程一直是用于理解大脑临界性的典型模型。这表明，可能需要使用具有不同相变的不同模型来解释数据。在这里，我们表明情况并非一定如此。通过使用属于与分支过程（平均场定向渗流）相同的普适类的两个不同模型，并像处理实验数据一样精确地处理模拟数据，我们再现了大多数实验结果。我们发现，对模型进行抽样并调整用于定义爆发的时间-bin（如实验数据中所做的那样）是改变临界点的明显指数的充分条件。此外，实验数据仅在相变附近的参数空间的非常窄的范围内得到再现。
