Department of Physics, Washington University, St. Louis, Missouri 63130
Department of Physics, Washington University, St. Louis, Missouri 63130.
J Neurosci. 2019 Jun 12;39(24):4738-4759. doi: 10.1523/JNEUROSCI.3163-18.2019. Epub 2019 Apr 5.
What information single neurons receive about general neural circuit activity is a fundamental question for neuroscience. Somatic membrane potential () fluctuations are driven by the convergence of synaptic inputs from a diverse cross-section of upstream neurons. Furthermore, neural activity is often scale-free, implying that some measurements should be the same, whether taken at large or small scales. Together, convergence and scale-freeness support the hypothesis that single recordings carry useful information about high-dimensional cortical activity. Conveniently, the theory of "critical branching networks" (one purported explanation for scale-freeness) provides testable predictions about scale-free measurements that are readily applied to fluctuations. To investigate, we obtained whole-cell current-clamp recordings of pyramidal neurons in visual cortex of turtles with unknown genders. We isolated fluctuations in below the firing threshold and analyzed them by adapting the definition of "neuronal avalanches" (i.e., spurts of population spiking). The fluctuations which we analyzed were scale-free and consistent with critical branching. These findings recapitulated results from large-scale cortical population data obtained separately in complementary experiments using microelectrode arrays described previously (Shew et al., 2015). Simultaneously recorded single-unit local field potential did not provide a good match, demonstrating the specific utility of Modeling shows that estimation of dynamical network properties from neuronal inputs is most accurate when networks are structured as critical branching networks. In conclusion, these findings extend evidence of critical phenomena while also establishing subthreshold pyramidal neuron fluctuations as an informative gauge of high-dimensional cortical population activity. The relationship between membrane potential () dynamics of single neurons and population dynamics is indispensable to understanding cortical circuits. Just as important to the biophysics of computation are emergent properties such as scale-freeness, where critical branching networks offer insight. This report makes progress on both fronts by comparing statistics from single-neuron whole-cell recordings with population statistics obtained with microelectrode arrays. Not only are fluctuations of somatic scale-free, they match fluctuations of population activity. Thus, our results demonstrate appropriation of the brain's own subsampling method (convergence of synaptic inputs) while extending the range of fundamental evidence for critical phenomena in neural systems from the previously observed mesoscale (fMRI, LFP, population spiking) to the microscale, namely, fluctuations.
神经元接收到的关于一般神经回路活动的信息是什么，这是神经科学的一个基本问题。躯体膜电位 () 的波动是由来自上游神经元的各种不同的突触输入的汇聚驱动的。此外，神经活动通常是无标度的，这意味着无论在大尺度还是小尺度上进行测量，某些测量值应该是相同的。汇聚和无标度性共同支持了这样一个假设，即单个记录携带有关高维皮质活动的有用信息。方便的是，“临界分支网络”的理论（无标度性的一种解释）为可应用于膜电位波动的无标度测量提供了可测试的预测。为了研究这个问题，我们从海龟的视觉皮层中获得了未知性别的全细胞膜片钳记录的锥体神经元。我们在低于放电阈值的情况下分离了 的波动，并通过适应“神经元喷发”（即种群爆发的突发）的定义来分析它们。我们分析的 波动是无标度的，并且与临界分支一致。这些发现与之前在使用微电极阵列进行的补充实验中获得的大皮层群体数据的结果一致（Shew 等人，2015）。同时记录的单个单位局部场电位并不能很好地匹配，这证明了 的具体用途。模型表明，当网络结构为临界分支网络时，从神经元输入中估计动态网络特性最为准确。总之，这些发现扩展了临界现象的证据，同时将亚阈值锥体神经元 波动确立为高维皮质群体活动的信息性量规。单个神经元的膜电位 () 动力学与群体动力学之间的关系对于理解皮质电路是不可或缺的。同样重要的是，计算的涌现性质，如无标度性，为临界分支网络提供了深入的见解。本报告通过比较微电极阵列获得的单细胞全细胞记录的统计数据与群体统计数据，在这两个方面都取得了进展。不仅是体细胞的波动是无标度的，而且与群体活动的波动相匹配。因此，我们的结果表明，在将大脑自身的子采样方法（突触输入的汇聚）应用于微尺度（即 波动）的同时，扩展了神经系统中临界现象的基本证据范围，从以前观察到的中尺度（fMRI、LFP、群体爆发）扩展到了微尺度。
