Department of Physics, Emory University, Atlanta, Georgia 30322, USA.
Initiative in Theory and Modeling of Living Systems, Emory University, Atlanta, Georgia 30322, USA.
Phys Rev Lett. 2021 Mar 19;126(11):118302. doi: 10.1103/PhysRevLett.126.118302.
Understanding the activity of large populations of neurons is difficult due to the combinatorial complexity of possible cell-cell interactions. To reduce the complexity, coarse graining had been previously applied to experimental neural recordings, which showed over two decades of apparent scaling in free energy, activity variance, eigenvalue spectra, and correlation time, hinting that the mouse hippocampus operates in a critical regime. We model such data by simulating conditionally independent binary neurons coupled to a small number of long-timescale stochastic fields and then replicating the coarse-graining procedure and analysis. This reproduces the experimentally observed scalings, suggesting that they do not require fine-tuning of internal parameters, but will arise in any system, biological or not, where activity variables are coupled to latent dynamic stimuli. Parameter sweeps for our model suggest that emergence of scaling requires most of the cells in a population to couple to the latent stimuli, predicting that even the celebrated place cells must also respond to nonplace stimuli.
理解大量神经元的活动是困难的，因为可能的细胞间相互作用具有组合复杂性。为了降低复杂性，以前曾对实验性神经记录进行了粗粒化处理，这表明自由能、活动方差、特征值谱和相关时间都有超过二十年的明显标度，这表明小鼠海马体在临界状态下运行。我们通过模拟条件独立的二进制神经元与少数长时标随机场的耦合，然后复制粗粒化过程和分析来对这些数据进行建模。这再现了实验中观察到的标度，表明它们不需要对内部参数进行微调，而是会出现在任何系统中，无论是生物系统还是非生物系统，只要活动变量与潜在的动态刺激耦合。我们模型的参数扫描表明，标度的出现需要群体中的大多数细胞与潜在的刺激耦合，这表明即使是著名的位置细胞也必须对非位置刺激做出反应。
