Scale-invariant neuronal avalanche dynamics and the cut-off in size distributions.
Scale-invariant neuronal avalanche dynamics and the cut-off in size distributions.
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比例不变的神经元雪崩动力学和大小分布的截止。
DOI：
10.1371/journal.pone.0099761
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Plenz D
中科院分区：
文献类型：
--
作者：
Yu S;Klaus A;Yang H;Plenz D
皮质动力学的识别极大地受益于同时记录尽可能多的神经元。然而，当前的技术仅提供了对哺乳动物皮层的不完整访问，需要从中得出有关动力学的充分结论。在这里，我们确定了通过使用有限数量的电极进行二次采样引入的约束，即空间“窗口”，以实现充分表征的关键动力学——神经元雪崩。使用长期植入的 96 微电极阵列记录两只清醒的猕猴在休息期间的运动前皮层和前额皮质的局部场电位 (LFP)。 LFP (nLFP) 中的负偏转是在阵列的完整和紧凑子区域上识别的，通过电极数量 N (10-95)（即窗口大小）进行量化。时空 nLFP 簇组织为神经元雪崩，即簇大小的概率 p(s) 在 N 范围内始终遵循指数为 -1.5 的幂律，超过该指数 p(s) 下降得更急剧，产生随 N 和 LFP 滤波器参数变化的“截止”。大小为 s≤N 的簇主要由来自独特的、非重复的皮质位点的 nLFP 组成，它们是从附近位点之间的局部传播中出现的，并携带有关簇组织的空间信息。相反，大小为 s>N 的簇主要由重复的位点激活主导，并且携带很少的空间信息，反映了严重扭曲的采样条件。我们的发现在神经元电极网络模型中得到了证实。因此，雪崩分析需要限制在观察窗口的大小范围内，以揭示由局部展开的、主要是前馈神经元级联产生的潜在的尺度不变组织。
Identification of cortical dynamics strongly benefits from the simultaneous recording of as many neurons as possible. Yet current technologies provide only incomplete access to the mammalian cortex from which adequate conclusions about dynamics need to be derived. Here, we identify constraints introduced by sub-sampling with a limited number of electrodes, i.e. spatial ‘windowing’, for well-characterized critical dynamics―neuronal avalanches. The local field potential (LFP) was recorded from premotor and prefrontal cortices in two awake macaque monkeys during rest using chronically implanted 96-microelectrode arrays. Negative deflections in the LFP (nLFP) were identified on the full as well as compact sub-regions of the array quantified by the number of electrodes N (10–95), i.e., the window size. Spatiotemporal nLFP clusters organized as neuronal avalanches, i.e., the probability in cluster size, p(s), invariably followed a power law with exponent −1.5 up to N, beyond which p(s) declined more steeply producing a ‘cut-off’ that varied with N and the LFP filter parameters. Clusters of size s≤N consisted mainly of nLFPs from unique, non-repeated cortical sites, emerged from local propagation between nearby sites, and carried spatial information about cluster organization. In contrast, clusters of size s>N were dominated by repeated site activations and carried little spatial information, reflecting greatly distorted sampling conditions. Our findings were confirmed in a neuron-electrode network model. Thus, avalanche analysis needs to be constrained to the size of the observation window to reveal the underlying scale-invariant organization produced by locally unfolding, predominantly feed-forward neuronal cascades.
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