TY - JOUR A1 - De Blasi, Stefano A1 - Ciba, Manuel A1 - Bahmer, Andreas A1 - Thielemann, Christiane T1 - Total spiking probability edges: A cross-correlation based method for effective connectivity estimation of cortical spiking neurons JF - Journal of Neuroscience Methods N2 - Background: 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. New method: 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. Results: 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. Comparison of existing methods: 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. Conclusions: 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. KW - Connectivity Estimation KW - Neuronal Networks KW - Parallel Spike Trains KW - Inhibitory and Excitatory KW - Neuronales Netz Y1 - 2019 U6 - https://doi.org/10.1016/j.jneumeth.2018.11.013 VL - 312 IS - 312 SP - 169 EP - 181 ER - TY - JOUR A1 - Ciba, Manuel A1 - Isomura, Takuya A1 - Jimbo, Yasuhiko A1 - Bahmer, Andreas A1 - Thielemann, Christiane T1 - Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony JF - Journal of Neuroscience Methods N2 - Background: Synchrony within neuronal networks is thought to be a fundamental feature of neuronal networks. In order to quantify synchrony between spike trains, various synchrony measures were developed. Most of them are time scale dependent and thus require the setting of an appropriate time scale. Recently, alternative methods have been developed, such as the time scale independent SPIKE-distance by Kreuz et al. New method: In this study, a novel time-scale independent spike train synchrony measure called Spike-contrast is proposed. The algorithm is based on the temporal “contrast” (activity vs. non-activity in certain temporal bins) and not only provides a single synchrony value, but also a synchrony curve as a function of the bin size. Results: For most test data sets synchrony values obtained with Spike-contrast are highly correlated with those of the SPIKE-distance (Spearman correlation value of 0.99). Correlation was lower for data containing multiple time scales (Spearman correlation value of 0.89). When analyzing large sets of data, Spike-contrast performed faster. Comparison of existing method: Spike-contrast is compared to the SPIKE-distance algorithm. The test data consisted of artificial spike trains with various levels of synchrony, including Poisson spike trains and bursts, spike trains from simulated neuronal Izhikevich networks, and bursts made of smaller bursts (sub-bursts). Conclusions: The high correlation of Spike-contrast with the established SPIKE-distance for most test data, suggests the suitability of the proposed measure. Both measures are complementary as SPIKE-distance provides a synchrony profile over time, whereas Spike-contrast provides a synchrony curve over bin size. KW - Synchrony KW - Neuronal Networks KW - Point processes KW - Time series analysis KW - Parallel spike trains KW - Neuronales Netz Y1 - 2017 U6 - https://doi.org/10.1016/j.jneumeth.2017.09.008 VL - 2018 IS - 293 SP - 136 EP - 143 ER - TY - CHAP A1 - Ciba, Manuel A1 - Bahmer, Andreas A1 - Thielemann, Christiane T1 - Application of spike train synchrony measure Spike‑contrast to quantify the effect of bicuculline on cortical networks grown on microelectrode arrays T2 - BMC Neuroscience KW - Mikroelektrode KW - Array Y1 - 2017 U6 - https://doi.org/10.1186/s12868-017-0372-1 VL - 2017 IS - 18 ER -