PLoS Comput Biol：数学模型阐述大脑单个神经元协作过程

大脑中神经元是如何进行彼此沟通的？一个普遍的理论认为单个细胞彼此之间不交换信号，而是细胞群体之间进行交换信号。来自日本、美国和德国的研究人员开发出了一种数学模型来测试这个假设。他们的研究结果已发表在PLoS Computational Biology杂志上。

8个模拟的神经元相互作用

大脑新皮层的神经元具有较高的脑功能，与成千上万的其他神经元相接触，收到许多来自其他神经元的输入信号。此前，用信号干预细胞的工作方式已经非常困难。现在日本理化学研究所脑科学研究所（BSI）的科学家们加入了德国Forschungszentrum Julich研究中心与美国波士顿麻省理工学院研究团队一起开发了一种数学模型，借此可以阐明神经元之间的相互协作方式。

BSI的英明岛崎博士解释说“从并行计算的许多信号来看，新方法能得到有关神经元是否单独沟通或作为一个群体进行沟通的信息。此外，考虑到这些细胞群体是不固定的，细胞群体可以在毫秒内自行灵活组织成不同细胞团体，这一过程取决于大脑当前的需求”。

Forschungszentrum Julich研究中心的Sonja Grün教授希望该方法可以帮助研究人员证明动态细胞群体的存在，并明确了解其对某些行为的活性。科学家已经证明当动物接受到外界信号时，神经元能相互作用导致动物有更快或更敏感的反应。

在未来，科学家希望能使用他们发明的方法对数百个神经元同时进行信号记录。 (生物谷 Bioon.com)

doi:10.1371/journal.pcbi.1002385
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State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

Hideaki Shimazaki1*, Shun-ichi Amari1, Emery N. Brown2,3,4, Sonja Grün1,5,6

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand.