Addressing skepticism of the critical brain hypothesis.
Addressing skepticism of the critical brain hypothesis.
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DOI：
10.3389/fncom.2022.703865
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发表时间：
2022
影响因子：
3.2
通讯作者：
中科院分区：
文献类型：
--
作者：
活的神经网络在临界相变点附近运行的假设已经得到了大量的讨论。这个“临界假设”是潜在的重要性，因为实验和理论表明，最佳的信息处理和健康与临界点附近的操作有关。尽管这一想法很有前途，但也有一些反对意见。虽然早期的反对意见已经得到解决，但最近对图布尔和德斯特什的批评还没有得到充分满足。本文的目的是描述他们的反对意见，并提供回应。他们的第一个反对意见是，著名的布鲁内尔模型的皮层网络没有显示在其相变附近的互信息的峰值，在明显的矛盾的临界性假设。在响应中，我表明，它确实有这样一个峰值附近的相变点，只要它不是强烈驱动的随机输入。他们的第二个反对意见是，即使是像抛硬币这样简单的模型也可以满足多个临界标准。这表明，声称存在于皮层网络中的紧急临界性只是通过阈值的随机行走的结果。作为回应，我表明，虽然这样的过程可以产生许多签名的关键性，这些签名（1）不出现从集体的相互作用，（2）不支持信息处理，（3）不具有长期的时间相关性。由于实验表明这三个特征在活的神经网络中始终存在，因此这种随机游走模型是不够的。尽管如此，我的结论是，这些反对意见对提炼研究问题是有价值的，应该作为科学过程的一部分受到欢迎。
The hypothesis that living neural networks operate near a critical phase transition point has received substantial discussion. This “criticality hypothesis” is potentially important because experiments and theory show that optimal information processing and health are associated with operating near the critical point. Despite the promise of this idea, there have been several objections to it. While earlier objections have been addressed already, the more recent critiques of Touboul and Destexhe have not yet been fully met. The purpose of this paper is to describe their objections and offer responses. Their first objection is that the well-known Brunel model for cortical networks does not display a peak in mutual information near its phase transition, in apparent contradiction to the criticality hypothesis. In response I show that it does have such a peak near the phase transition point, provided it is not strongly driven by random inputs. Their second objection is that even simple models like a coin flip can satisfy multiple criteria of criticality. This suggests that the emergent criticality claimed to exist in cortical networks is just the consequence of a random walk put through a threshold. In response I show that while such processes can produce many signatures criticality, these signatures (1) do not emerge from collective interactions, (2) do not support information processing, and (3) do not have long-range temporal correlations. Because experiments show these three features are consistently present in living neural networks, such random walk models are inadequate. Nevertheless, I conclude that these objections have been valuable for refining research questions and should always be welcomed as a part of the scientific process.
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