Martignon L, Deco G, Laskey K, Diamond M, Freiwald W, Vaadia E
Max Planck Institute for Human Development, Berlin, Germany.
Neural Comput. 2000 Nov;12(11):2621-53. doi: 10.1162/089976600300014872.
Recent advances in the technology of multiunit recordings make it possible to test Hebb's hypothesis that neurons do not function in isolation but are organized in assemblies. This has created the need for statistical approaches to detecting the presence of spatiotemporal patterns of more than two neurons in neuron spike train data. We mention three possible measures for the presence of higher-order patterns of neural activation--coefficients of log-linear models, connected cumulants, and redundancies--and present arguments in favor of the coefficients of log-linear models. We present test statistics for detecting the presence of higher-order interactions in spike train data by parameterizing these interactions in terms of coefficients of log-linear models. We also present a Bayesian approach for inferring the existence or absence of interactions and estimating their strength. The two methods, the frequentist and the Bayesian one, are shown to be consistent in the sense that interactions that are detected by either method also tend to be detected by the other. A heuristic for the analysis of temporal patterns is also proposed. Finally, a Bayesian test is presented that establishes stochastic differences between recorded segments of data. The methods are applied to experimental data and synthetic data drawn from our statistical models. Our experimental data are drawn from multiunit recordings in the prefrontal cortex of behaving monkeys, the somatosensory cortex of anesthetized rats, and multiunit recordings in the visual cortex of behaving monkeys.
多单元记录技术的最新进展使得检验赫布假说成为可能，该假说认为神经元并非孤立运作，而是以集合的形式组织起来。这就产生了对统计方法的需求，以便在神经元尖峰序列数据中检测两个以上神经元的时空模式的存在。我们提到了三种用于检测神经激活高阶模式存在的可能度量——对数线性模型的系数、连接累积量和冗余度——并给出了支持对数线性模型系数的论据。我们通过根据对数线性模型的系数对这些相互作用进行参数化，给出了用于检测尖峰序列数据中高阶相互作用存在的检验统计量。我们还提出了一种贝叶斯方法，用于推断相互作用的存在与否并估计其强度。结果表明，频率主义方法和贝叶斯方法在某种意义上是一致的，即任何一种方法检测到的相互作用往往也能被另一种方法检测到。我们还提出了一种用于分析时间模式的启发式方法。最后，给出了一种贝叶斯检验，用于确定记录的数据段之间的随机差异。这些方法被应用于实验数据和从我们的统计模型中生成的合成数据。我们的实验数据取自行为猴子前额叶皮层的多单元记录、麻醉大鼠体感皮层的多单元记录以及行为猴子视觉皮层的多单元记录。
