粗--在大量神经元中的颗粒化、固定点和缩放
Coarse--graining, fixed points, and scaling in a large population of neurons
摘要：
我们开发了一个现象学的粗粒化程序的活动在一个大型网络的神经元，并将其应用于记录从人口的1000+细胞在海马。粗粒度变量的分布似乎接近一个固定的非高斯形式，我们看到的证据，在静态和动态量的缩放。这些结果表明，网络的集体行为是由一个非平凡的不动点。
英文摘要：
We develop a phenomenological coarse--graining procedure for activity in a large network of neurons, and apply this to recordings from a population of 1000+ cells in the hippocampus. Distributions of coarse--grained variables seem to approach a fixed non--Gaussian form, and we see evidence of scaling in both static and dynamic quantities. These results suggest that the collective behavior of the network is described by a non--trivial fixed point.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
