State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data.
State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data.
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DOI：
10.1371/journal.pcbi.1002385
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发表时间：
2012
影响因子：
4.3
通讯作者：
Grün S
中科院分区：
文献类型：
--
作者：
Shimazaki H;Amari S;Brown EN;Grün S
多个神经元的脉冲活动之间精确的脉冲协调被认为是活跃细胞组合中协调的网络活动的一种标志。脉冲相关性分析旨在通过检测同时记录的多个神经脉冲序列中的过度脉冲同步来识别这种协同网络活动。协同活动预计会在行为和认知过程中动态组织；因此，当前可用的分析技术必须加以扩展，以便能够同时估计神经元之间多种随时间变化的脉冲相互作用。特别是，新方法必须利用对多个神经元的同时观测，处理它们的高阶相关性，这是仅通过成对分析无法揭示的。在本文中，我们开发了一种通过状态空间分析来估计随时间变化的脉冲相互作用的方法。离散化的并行脉冲序列使用对数线性模型被建模为多元二值过程，该模型在信息几何框架中提供了一种明确定义的高阶脉冲相关性度量。我们构建了一个递归贝叶斯滤波器/平滑器来提取脉冲相互作用参数。这种方法可以同时估计多个单个神经元的动态成对脉冲相互作用，从而将对多个神经脉冲序列数据的伊辛/自旋玻璃模型分析扩展到非平稳分析。此外，该方法还可以估计动态高阶脉冲相互作用。为了验证模型中高阶项的合理性，我们构建了一种近似方法来评估对脉冲数据的拟合优度。此外，我们制定了一种测试方法，用于检测即使在非平稳脉冲数据（例如，来自清醒行为动物的数据）中是否存在高阶脉冲相关性。所提出方法的实用性通过具有已知潜在相关动态的模拟脉冲数据进行了测试。最后，我们将这些方法应用于从清醒猴子的运动皮层同时记录的神经脉冲数据，并证明高阶脉冲相关性与行为需求相关地动态组织。
近半个世纪前，加拿大心理学家D. O. 赫布推测，由于网络受到重复的感觉刺激导致突触权重发生变化（赫布学习规则），皮质递归网络中会形成紧密连接的细胞组合。因此，这种组合在处理感觉或行为信息时的激活很可能通过参与神经元精确协调的脉冲活动来体现。然而，用于多个并行神经脉冲数据的现有分析技术不允许我们揭示瞬时活跃组合的详细结构，而这种结构是由它们动态的成对和高阶脉冲相关性所表明的。在这里，我们构建了一个动态脉冲相互作用的状态空间模型，并提出了一种递归贝叶斯方法，使得能够以随时间变化的方式追踪表现出这种精确协调脉冲活动的多个神经元。我们还制定了一个关于潜在动态脉冲相关性的假设检验，这使我们能够检测与行为事件相关联而被激活的组合。因此，所提出的方法可以作为一种有用的工具来检验赫布的细胞组合假设。
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. Nearly half a century ago, the Canadian psychologist D. O. Hebb postulated the formation of assemblies of tightly connected cells in cortical recurrent networks because of changes in synaptic weight (Hebb's learning rule) by repetitive sensory stimulation of the network. Consequently, the activation of such an assembly for processing sensory or behavioral information is likely to be expressed by precisely coordinated spiking activities of the participating neurons. However, the available analysis techniques for multiple parallel neural spike data do not allow us to reveal the detailed structure of transiently active assemblies as indicated by their dynamical pairwise and higher-order spike correlations. Here, we construct a state-space model of dynamic spike interactions, and present a recursive Bayesian method that makes it possible to trace multiple neurons exhibiting such precisely coordinated spiking activities in a time-varying manner. We also formulate a hypothesis test of the underlying dynamic spike correlation, which enables us to detect the assemblies activated in association with behavioral events. Therefore, the proposed method can serve as a useful tool to test Hebb's cell assembly hypothesis.
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