论文标题
临界点附近的有限大小相关行为：一种用于监视神经网络状态的简单指标
Finite-size correlation behavior near a critical point: a simple metric for monitoring the state of a neural network
论文作者
论文摘要
在本文中，为推断神经元网络的动态状态提出了相关度量$κ_C$。 $κ_C$是根据与观测区域的相关长度的缩放长度计算得出的，该长度与连续相变的临界点相近和距离显示出质量上不同的行为。该实现首先是在神经元网络模型上研究的，该模型与从神经雪崩分析中获得的结果相吻合，从而很好地表征了网络的临界状态。该方法通过来自公共可用数据库的行为小鼠的大脑光遗传学记录进一步测试。讨论了其与当前可用的光学成像技术一起使用的潜在应用和局限性。
In this article, a correlation metric $κ_C$ is proposed for the inference of the dynamical state of neuronal networks. $κ_C$ is computed from the scaling of the correlation length with the size of the observation region, which shows qualitatively different behavior near and away from the critical point of a continuous phase transition. The implementation is first studied on a neuronal network model, where the results of this new metric coincide with those obtained from neuronal avalanche analysis, thus well characterizing the critical state of the network. The approach is further tested with brain optogenetic recordings in behaving mice from a publicly available database. Potential applications and limitations for its use with currently available optical imaging techniques are discussed.
