BioMEMS Lab, University of Applied Sciences Aschaffenburg, 63743 Aschaffenburg, Germany.
BioMEMS Lab, University of Applied Sciences Aschaffenburg, 63743 Aschaffenburg, Germany.
J Neurosci Methods. 2019 Jan 15;312:169-181. doi: 10.1016/j.jneumeth.2018.11.013. Epub 2018 Nov 27.
Connectivity is a relevant parameter for the information flow within neuronal networks. Network connectivity can be reconstructed from recorded spike train data. Various methods have been developed to estimate connectivity from spike trains.
In this work, a novel effective connectivity estimation algorithm called Total Spiking Probability Edges (TSPE) is proposed and evaluated. First, a cross-correlation between pairs of spike trains is calculated. Second, to distinguish between excitatory and inhibitory connections, edge filters are applied on the resulting cross-correlogram.
TSPE was evaluated with large scale in silico networks and enables almost perfect reconstructions (true positive rate of approx. 99% at a false positive rate of 1% for low density random networks) depending on the network topology and the spike train duration. A distinction between excitatory and inhibitory connections was possible. TSPE is computational effective and takes less than 3 min on a high-performance computer to estimate the connectivity of an 1 h dataset of 1000 spike trains.
TSPE was compared with connectivity estimation algorithms like Transfer Entropy based methods, Filtered and Normalized Cross-Correlation Histogram and Normalized Cross-Correlation. In all test cases, TSPE outperformed the compared methods in the connectivity reconstruction accuracy.
The results show that the accuracy of functional connectivity estimation of large scale neuronal networks has been enhanced by TSPE compared to state of the art methods. Furthermore, TSPE enables the classification of excitatory and inhibitory synaptic effects.
连通性是神经元网络内信息流的一个相关参数。网络连通性可以从记录的尖峰火车数据中重建。已经开发了各种方法来从尖峰火车中估计连通性。
在这项工作中，提出并评估了一种称为总尖峰概率边缘（TSPE）的新的有效连通性估计算法。首先，计算一对尖峰火车之间的互相关。其次，为了区分兴奋性和抑制性连接，在得到的互相关图上应用边缘滤波器。
TSPE 用大规模的模拟网络进行了评估，并且可以实现几乎完美的重建（在低密度随机网络中，假阳性率为 1%时，真阳性率约为 99%），这取决于网络拓扑和尖峰火车持续时间。能够区分兴奋性和抑制性连接。TSPE 在计算上是有效的，在高性能计算机上只需不到 3 分钟即可估计 1000 个尖峰火车 1 小时数据集的连通性。
TSPE 与基于转移熵的方法、滤波和归一化互相关直方图以及归一化互相关等连通性估计算法进行了比较。在所有测试案例中，TSPE 在连通性重建准确性方面均优于比较方法。
结果表明，与最先进的方法相比，TSPE 提高了大规模神经元网络功能连通性估计的准确性。此外，TSPE 能够对兴奋性和抑制性突触效应进行分类。
