@article{DeBlasiCibaBahmeretal.2019, author = {De Blasi, Stefano and Ciba, Manuel and Bahmer, Andreas and Thielemann, Christiane}, title = {Total spiking probability edges: A cross-correlation based method for effective connectivity estimation of cortical spiking neurons}, series = {Journal of Neuroscience Methods}, volume = {312}, journal = {Journal of Neuroscience Methods}, number = {312}, doi = {10.1016/j.jneumeth.2018.11.013}, pages = {169 -- 181}, year = {2019}, abstract = {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.}, subject = {Neuronales Netz}, language = {en} } @article{CibaIsomuraJimboetal.2017, author = {Ciba, Manuel and Isomura, Takuya and Jimbo, Yasuhiko and Bahmer, Andreas and Thielemann, Christiane}, title = {Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony}, series = {Journal of Neuroscience Methods}, volume = {2018}, journal = {Journal of Neuroscience Methods}, number = {293}, doi = {10.1016/j.jneumeth.2017.09.008}, pages = {136 -- 143}, year = {2017}, abstract = {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.}, subject = {Neuronales Netz}, language = {en} } @inproceedings{CibaBahmerThielemann2017, author = {Ciba, Manuel and Bahmer, Andreas and Thielemann, Christiane}, title = {Application of spike train synchrony measure Spike‑contrast to quantify the effect of bicuculline on cortical networks grown on microelectrode arrays}, series = {BMC Neuroscience}, volume = {2017}, booktitle = {BMC Neuroscience}, number = {18}, doi = {10.1186/s12868-017-0372-1}, pages = {269}, year = {2017}, subject = {Mikroelektrode}, language = {en} }