Neuronal avalanches in the neocortex

在过去的一年里，我们在神经元雪崩研究方面取得了几项重大进展： 1.我们第一次证明了具有雪崩动力学的皮层网络保持了兴奋和抑制的平衡，在这种平衡下，信息容量和信息处理得到了优化（Shew等人，2010年）。 总结： 皮层网络可以产生的神经活动模式的库限制了网络传输和处理信息的能力。 在这里，我们测量的活动模式获得的多站点局部场电位（LFP）记录在皮层文化，氨基甲酸乙酯麻醉大鼠，清醒的猕猴。 首先，我们量化的信息容量的模式剧目的持续和刺激诱发的活动使用香农熵。 接下来，我们用互信息量化了刺激和反应之间的信息传递效率。 通过系统地改变兴奋/抑制（E/I）在体外和网络模型中的比例，我们发现，在一个特定的中间E/I，在正在进行的活动出现神经元雪崩的信息容量和信息传输最大化。 接下来，我们使用我们的体外和模型结果来正确预测体内信息容量和正在进行的活动中神经元组之间的相互作用。我们的实验和模型之间的密切协议表明，神经元雪崩和峰值信息容量的出现是由于临界性，是具有平衡的E/I的皮层网络的一般属性。 2.我们提供了详尽的统计分析，显示神经元雪崩的特征在于指示临界状态动力学的真幂定律（Klaus等人，2010年）。 总结： 据报道，皮层网络中神经元雪崩的大小分布遵循幂律分布，斜率接近-1.5，这反映了自发神经元活动中的长程空间相关性。 然而，在经验数据中识别幂律标度可能是困难的，有时甚至是有争议的。 在本研究中，我们通过更严格的统计分析来检验神经元雪崩的幂律假设。 特别是，我们执行了以下步骤：（i）分析有限尺寸标度以识别神经元雪崩中的无标度动力学，（ii）模型参数估计以确定幂律的特定指数，以及（iii）将幂律与替代模型分布进行比较。 与临界状态动力学一致，雪崩尺寸分布表现出强大的缩放行为，其中最大雪崩尺寸仅受采样的空间范围（"有限尺寸"效应）的限制。 这种无标度动力学表明，幂律是雪崩大小分布的模型。 使用Kolmogorov-Smirnov统计量和最大似然方法，我们发现斜率接近-1.5，这与以前的报告一致。 最后，幂律模型的神经元雪崩进行了比较，指数和各种重尾分布的基础上的Kolmogorov-Smirnov距离，并通过使用对数似然比检验。 幂律分布和指数截止没有提供了一个显着更好的适合集群的大小分布在神经元雪崩比指数，对数正态分布和伽玛分布。总之，我们的研究结果强烈支持幂律缩放在神经元雪崩，提供了进一步的证据，在皮层表层的临界状态动力学。 3.在我们努力改善系统研究的体外条件下，我们现在已经提供了一个详细的协议，视频支持和错误分析，以准备我们独特的皮质-前脑共培养系统。 总结： 一个强大的方法来研究神经元雪崩，即尺度不变的时空活动爆发，在皮层的临界状态动力学的指示。 雪崩在培养的皮层的发育的表层中自发出现，这允许在精确控制的条件下用平面集成多电极阵列（MEA）长期测量活性（Plenz等人，2011年）。

During the last year, we made several major advances in neuronal avalanche research: 1. We demonstrated for the first time that cortical networks with avalanche dynamics maintain a balance of excitation and inhibition at which information capacity and information processing is optimized (Shew et al., 2010). Summary: The repertoire of neural activity patterns that a cortical network can produce constrains the networks ability to transfer and process information. Here, we measured activity patterns obtained from multi-site local field potential (LFP) recordings in cortex cultures, urethane anesthetized rats, and awake macaque monkeys. First, we quantified the information capacity of the pattern repertoire of ongoing and stimulus-evoked activity using Shannon entropy. Next, we quantified the efficacy of information transmission between stimulus and response using mutual information. By systematically changing the ratio of excitation/inhibition (E/I) in vitro and in a network model, we discovered that both information capacity and information transmission are maximized at a particular intermediate E/I, at which ongoing activity emerges as neuronal avalanches. Next, we used our in vitro and model results to correctly predict in vivo information capacity and interactions between neuronal groups during ongoing activity. Close agreement between our experiments and model suggest that neuronal avalanches and peak information capacity arise due to criticality and are general properties of cortical networks with balanced E/I. 2. We provided an exhaustive statistical analysis showing that neuronal avalanches are characterized by true power laws indicative of critical state dynamics (Klaus et al., 2010). Summary: The size distribution of neuronal avalanches in cortical networks has been reported to follow a power law distribution with slope close to -1.5, which is a reflection of long-range spatial correlations in spontaneous neuronal activity. However, identifying power law scaling in empirical data can be difficult and sometimes controversial. In the present study, we tested the power law hypothesis for neuronal avalanches by using more stringent statistical analysis. In particular, we performed the following steps: (i) analysis of finite-size scaling to identify scale-free dynamics in neuronal avalanches, (ii) model parameter estimation to determine the specific exponent of the power law, and (iii) comparison of the power law to alternative model distributions. Consistent with critical state dynamics, avalanche size distributions exhibited robust scaling behavior in which the maximum avalanche size was limited only by the spatial extent of sampling ("finite size" effect). This scale-free dynamics suggests the power law as a model for the distribution of avalanche sizes. Using both the Kolmogorov-Smirnov statistic and a maximum likelihood approach, we found the slope to be close to -1.5, which is in line with previous reports. Finally, the power law model for neuronal avalanches was compared to the exponential and to various heavy-tail distributions based on the Kolmogorov-Smirnov distance and by using a log-likelihood ratio test. Both the power law distribution with and without exponential cutoff provided a significantly better fit to the cluster size distributions in neuronal avalanches than the exponential, the lognormal and the gamma distribution. In summary, our findings strongly support the power law scaling in neuronal avalanches, providing further evidence for critical state dynamics in superficial layers of cortex. 3. In our effort to improve in vitro conditions for systems research, we have now provided a detailed protocol with video support and error analysis for the preparation of our unique cortex-forebrain co-culture systems. Summary: A robust way to study neuronal avalanches, i.e. scale-invariant spatio-temporal activity bursts, indicative of critical state dynamics in cortex. Avalanches emerge spontaneously in developing superficial layers of cultured cortex which allows for long-term measurements of the activity with planar integrated multi-electrode arrays (MEA) under precisely controlled conditions (Plenz, et al., 2011).