The criticality hypothesis: how local cortical networks might optimize information processing
The criticality hypothesis: how local cortical networks might optimize information processing
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DOI：
10.1098/rsta.2007.2092
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发表时间：
2008-02-13
期刊：
影响因子：
5
通讯作者：
Beggs, John M.
中科院分区：
文献类型：
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作者：
Beggs, John M.
帕卡德、兰顿和考夫曼独立进行的早期理论和模拟工作表明，在“混沌边缘”，即在完全随机和无趣有序之间相变的临界点上，系统的适应性和计算能力将得到优化。这一具有挑衅性的假设受到了广泛关注，但支持这一假设的生物学实验相对较少。在这里，我们回顾了最近关于皮质神经元网络的实验，表明它们似乎在临界点附近运作。模拟研究捕获了这些数据的主要特征，并表明临界性可能允许皮质网络优化信息处理。这些模拟得出的预测可以在不久的将来进行测试，可能为临界假设提供进一步的实验证据。
Early theoretical and simulation work independently undertaken by Packard, Langton and Kauffman suggested that adaptability and computational power would be optimized in systems at the 'edge of chaos', at a critical point in a phase transition between total randomness and boring order. This provocative hypothesis has received much attention, but biological experiments supporting it have been relatively few. Here, we review recent experiments on networks of cortical neurons, showing that they appear to be operating near the critical point. Simulation studies capture the main features of these data and suggest that criticality may allow cortical networks to optimize information processing. These simulations lead to predictions that could be tested in the near future, possibly providing further experimental evidence for the criticality hypothesis.