@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} } @misc{DeBlasi2018, author = {De Blasi, Stefano}, title = {Simulation of Large Scale Neural Networks for Evaluation Applications}, series = {22nd International Student Conference on Electrical Engineering POSTER 2018}, volume = {2018}, journal = {22nd International Student Conference on Electrical Engineering POSTER 2018}, number = {POSTER 2018}, pages = {1 -- 6}, year = {2018}, abstract = {Understanding the complexity of biological neural networks like the human brain is one of the scientific challenges of our century. The organization of the brain can be described at different levels, ranging from small neural networks to entire brain regions. Existing methods for the description of functionally or effective connectivity are based on the analysis of relations between the activities of different neural units by detecting correlations or information flow. This is a crucial step in understanding neural disorders like Alzheimer's disease and their causative factors. To evaluate these estimation methods, it is necessary to refer to a neural network with known connectivity, which is typically unknown for natural biological neural networks. Therefore, network simulations, also in silico, are available. In this work, the in silico simulation of large scale neural networks is established and the influence of different topologies on the generated patterns of neuronal signals is investigated. The goal is to develop standard evaluation methods for neurocomputational algorithms with a realistic large scale model to enable benchmarking and comparability of different studies.}, subject = {Neuronales Netz}, language = {en} }