Neuronal avalanches in the neocortex

我们以前将神经元雪崩描述为大脑皮层的一种基本同步动力学。 在过去的一年里，我们在神经元雪崩研究方面取得了两大进展： 1.我们与杜克大学的Nicolelis实验室合作证明，清醒猴子的持续活动是由神经元雪崩组成的。这引入了临界性，作为清醒状态的精确、定量的框架，允许皮质在发育和进化期间扩展，而不需要对结构进行根本改变(Petermann等，2009，PNAS)。 摘要： 自发性神经元活动是大脑皮层的一种重要特性，但其时空组织和动力学结构尚不清楚。对简化系统的组织培养、急性切片和麻醉大鼠的研究表明，自发活动在空间和时间上形成了特有的簇，称为神经元雪崩。建模研究表明，具有这一特性的网络处于优化输入处理、信息存储和传输的关键状态，但雪崩与完全功能的大脑系统的相关性一直存在争议。在这里，我们展示了清醒恒河猴正在进行的皮质同步带有神经元雪崩的特征。负LFP偏转(NLFP)与神经元尖峰相关，波幅随局部群体尖峰频率和同步性的增加而增加。这些nLFP形成的神经元雪崩在空间和时间上是尺度不变的，并且相对于nLFP检测的阈值是不变的。这种新的维度，阈值不变性，描述了一种分形组织：较小的nLFP嵌入到较大nLFP的集群中，而不破坏动力学的空间和时间标度不变性。这些发现表明，正在进行的大脑皮层同步的组织在时间、空间和局部神经元群大小的三个基本维度上是尺度不变的。这种规模不变性具有个体发生和系统发展的含义，因为它允许在不从根本上重组系统的情况下增加网络容量。 2.我们发展了新的理论，从观察到的网络动力学重建神经元网络的功能结构。这一一般理论优于目前使用基于相关性的方法从高度并行的大脑活动记录(例如，脑磁图、脑电、功能磁共振)重建功能网络图的尝试。这是解开个人雪崩组织的一个重要里程碑(Pajevic和Plenz，2009，PLoS Comp Biol)： 摘要： 级联活动通常存在于具有定向相互作用的复杂系统中，如代谢网络、神经网络或疾病在社会网络中的传播。通过重构底层的功能可以获得对系统组织的实质性洞察 从观察到的活动级联而来的网络架构。在这里，我们关注贝叶斯方法，并通过引入迭代贝叶斯(IB)和后验加权平均(PWA)方法来减少它们的计算量。我们证明了PWA，当应用于近临界级联时 动力学，可以以非参数形式转换，我们称之为归一化计数(NC)算法。NC从次临界、临界和超临界级联动力学中高效地重建随机和小世界功能网络拓扑和体系结构，并比常用的相关方法产生显著的改进。利用实验数据，NC识别了具有神经元雪崩动力学的大脑皮层网络中的功能和结构小世界拓扑以及相应的流量。

We previously described neuronal avalanches as a fundamental synchronization dynamics of the cortex. During the last year, we made two major advances in neuronal avalanche research: 1. We demonstrated in collaboration with the Nicolelis lab at Duke that ongoing activity in awake monkeys is composed of neuronal avalanches. This introduces criticality as a precise, quantitative framework of the awake state that allows cortex to expand during development and evolution without fundamental changes in architecture (Petermann et al, 2009, PNAS). Summary: Spontaneous neuronal activity is an important property of the cerebral cortex but its spatiotemporal organization and dynamical framework remain poorly understood. Studies in reduced systems tissue cultures, acute slices, and anesthetized rats show that spontaneous activity forms characteristic clusters in space and time, called neuronal avalanches. Modeling studies suggest that networks with this property are poised at a critical state that optimizes input processing, information storage, and transfer, but the relevance of avalanches for fully functional cerebral systems has been controversial. Here we show that ongoing cortical synchronization in awake rhesus monkeys carries the signature of neuronal avalanches. Negative LFP deflections (nLFPs) correlate with neuronal spiking and increase in amplitude with increases in local population spike rate and synchrony. These nLFPs form neuronal avalanches that are scale-invariant in space and time and with respect to the threshold of nLFP detection. This new dimension, threshold invariance, describes a fractal organization: smaller nLFPs are embedded in clusters of larger ones without destroying the spatial and temporal scale-invariance of the dynamics. These findings suggest an organization of ongoing cortical synchronization that is scale-invariant in its three fundamental dimensions time, space, and local neuronal group size. Such scale-invariance has ontogenetic and phylogenetic implications because it allows increases in network capacity without a fundamental reorganization of the system. 2. We developed new theory that reconstructs the functional architecture of neuronal networks from the observed network dynamics. This general theory is superior over current attempts to reconstruct functional network graphs from highly parrallel recordings of brain activity (e.g. MEG, EEG, fMRI) using correlation based approaches. This is an important milestone to unravel the organization of individual avalanches (Pajevic and Plenz, 2009, PLoS Comp Biol): Summary: Cascading activity is commonly found in complex systems with directed interactions such as metabolic networks, neuronal networks, or disease spreading in social networks. Substantial insight into a system's organization can be obtained by reconstructing the underlying functional network architecture from the observed activity cascades. Here we focus on Bayesian approaches and reduce their computational demands by introducing the Iterative Bayesian (IB) and Posterior Weighted Averaging (PWA) method. We show that PWA, when applied to near-critical cascading dynamics, can be cast in non-parametric form, which we call the normalized count (NC) algorithm. NC efficiently reconstructs random and small-world functional network topologies and architectures from sub-critical, critical, and super-critical cascading dynamics and yields significant improvements over commonly used correlation methods. Using experimental data, NC identified the functional and structural small-world topologies and corresponding traffic in cortical networks with neuronal avalanche dynamics.