论文标题
通过特征向量角度评估神经网络活动和连通性的统计相似性
Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles
论文作者
论文摘要
神经系统是网络，在许多研究方案中，多个网络之间的战略比较是普遍的任务。在这项研究中，我们构建了一个统计检验，用于比较代表神经网络成对方面的矩阵，特别是峰值活动与连通性之间的相关性。 “特征角测试”量化了两个矩阵的相似性，通过其排名特征向量之间的角度。我们使用相关的尖峰活动的随机模型来校准与相关矩阵一起使用的测试的行为，并证明它与经典的两样本测试（例如Kolmogorov-smirnov距离）进行了比较，从某种意义上说，它也能够评估成对的结构方面。此外，可以应用特征角测试的原理来比较某些类型网络的邻接矩阵的相似性。因此，该方法可用于定量探索与相同度量的连通性与活动之间的关系。通过在特定的突触重新启动干预之前和之后，将特征角测试应用于连通性矩阵和随机平衡网络模型的相关矩阵的比较，我们可以评估连接特征对相关活动的影响。特征角测试的潜在应用包括仿真实验，模型验证和数据分析。
Neural systems are networks, and strategic comparisons between multiple networks are a prevalent task in many research scenarios. In this study, we construct a statistical test for the comparison of matrices representing pairwise aspects of neural networks, in particular, the correlation between spiking activity and connectivity. The "eigenangle test" quantifies the similarity of two matrices by the angles between their ranked eigenvectors. We calibrate the behavior of the test for use with correlation matrices using stochastic models of correlated spiking activity and demonstrate how it compares to classical two-sample tests, such as the Kolmogorov-Smirnov distance, in the sense that it is able to evaluate also structural aspects of pairwise measures. Furthermore, the principle of the eigenangle test can be applied to compare the similarity of adjacency matrices of certain types of networks. Thus, the approach can be used to quantitatively explore the relationship between connectivity and activity with the same metric. By applying the eigenangle test to the comparison of connectivity matrices and correlation matrices of a random balanced network model before and after a specific synaptic rewiring intervention, we gauge the influence of connectivity features on the correlated activity. Potential applications of the eigenangle test include simulation experiments, model validation, and data analysis.
