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Elefante, S., Stadlbauer, S., Alexander, M. F., &; Schlögl, A. (n.d.). Cryo-EM software packages: A sys-admins point of view. In ASHP - Austrian-Slovenian HPC Meeting 2023 (pp. 42–42). Maribor, Slovenia: EuroCC.

Schlögl, A., Elefante, S., &; Hodirnau, V.-V. (n.d.). Running Windows-applications on a Linux HPC cluster using WINE. In ASHP - Austrian-Slovenian HPC Meeting 2023 (pp. 59–59). Maribor, Slovenia: EuroCC.

Schlögl, A., Hornoiu, A., Elefante, S., &; Stadlbauer, S. (2022). Where is the sweet spot? A procurement story of general purpose compute nodes. In ASHP - Austrian-Slovenian HPC Meeting 2022 (p. 7). Grundlsee, Austria: EuroCC Austria c/o Universität Wien.https://doi.org/10.25365/phaidra.337 

Schlögl, A., Elefante, S., Hornoiu, A., &; Stadlbauer, S. (2021). Managing software on a heterogenous HPC cluster. In ASHP – Austrian-Slovenian HPC Meeting 2021 (p. 5). Virtual: University of Ljubljana.https://doi.org/10.3359/2021hpc 

Zhang, X., Schlögl, A., Vandael, D. H., &; Jonas, P. M. (2021). MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo. Journal of Neuroscience Methods. Elsevier.https://doi.org/10.1016/j.jneumeth.2021.109125 

Rubel, P., Fayn, J., Macfarlane, P. W., Pani, D., Schlögl, A., &; Värri, A. (2021). The history and challenges of SCP-ECG: The standard communication protocol for computer-assisted electrocardiography. Hearts. MDPI.https://doi.org/10.3390/hearts2030031 

Guzmán, J., Schlögl, A., Espinoza Martinez, C., Zhang, X., Suter, B., &; Jonas, P. M. (2021). How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–C network. Nature Computational Science. Springer Nature.https://doi.org/10.1038/s43588-021-00157-1 

Guzmán, J., Schlögl, A., Espinoza Martinez, C., Zhang, X., Suter, B., &; Jonas, P. M. (2021). How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–C network. IST Austria.https://doi.org/10.15479/AT:ISTA:10110 

Schlögl, A., Kiss, J., &; Elefante, S. (Eds.). (2020). Austrian High-Performance-Computing meeting (AH). Presented at the AHPC: Austrian High-Performance-Computing Meeting, Klosterneuburg, Austria: IST Austria.https://doi.org/10.15479/AT:ISTA:7474 

Zhang, X., Schlögl, A., &; Jonas, P. M. (2020). Selective routing of spatial information flow from input to output in hippocampal granule cells. Neuron. Elsevier.https://doi.org/10.1016/j.neuron.2020.07.006 

Schlögl, A., Kiss, J., &; Elefante, S. (2019). Is Debian suitable for running an HPC Cluster? In AHP - Austrian HPC Meeting 2019  (p. 25). Grundlsee, Austria: Institut für Mathematik und wissenschaftliches Rechnen der Universität Graz.

Sauermann, S., David, V., Schlögl, A., Egelkraut, R., Frohner, M., Pohn, B.,https://doi.org/10.3233/978-1-61499-759-7-356__Mense, A. (2017). Biosignals standards and FHIR: The way to go (Vol. 236, pp. 356–362). Presented at the eHealth: Health Informatics Meets eHealth, Vienna, Austria: IOS Press.https://doi.org/10.3233/978-1-61499-759-7-356 

Schlögl, A., &; Kiss, J. (2017). Scientific Computing at IST Austria. In AHP – Austrian HPC Meeting 2017 (p. 28). Grundlsee, Austria: FSP Scientific Computing.

Rubel, P., Pani, D., Schlögl, A., Fayn, J., Badilini, F., Macfarlane, P., &; Varri, A. (2016). SCP-ECG .0: An enhanced standard communication protocol for computer-assisted electrocardiography. In 2016 Computing in Cardiology Conference (Vol. 43, pp. 309–312). Vancouver, Canada: Computing in Cardiology.https://doi.org/10.22489/cinc.2016.090-500 

Guzmán, J., Schlögl, A., Frotscher, M., &; Jonas, P. M. (2016). Synaptic mechanisms of pattern completion in the hippocampal C network. Science. American Association for the Advancement of Science.https://doi.org/10.1126/science.aaf1836 

Schlögl, A., &; Stadlbauer, S. (2016). High performance computing at IST Austria: Modelling the human hippocampus. In AHP - Austrian HPC Meeting 2016 (p. 37). Grundlsee, Austria: VSC - Vienna Scientific Cluster.

Körner, C., Braunstein, V., Stangl, M., Schlögl, A., Neuper, C., &; Ischebeck, A. (2014). Sequential effects in continued visual search: Using fixation-related potentials to compare distractor processing before and after target detection. Psychophysiology. Wiley-Blackwell.https://doi.org/10.1111/psyp.12062 

Guzmán, J., Schlögl, A., &; Schmidt Hieber, C. (2014). Stimfit: Quantifying electrophysiological data with Python. Frontiers in Neuroinformatics. Frontiers Research Foundation.https://doi.org/10.3389/fninf.2014.00016 

Schlögl, A., Jonas, P. M., Schmidt-Hieber, C., &; Guzman, S. J. (2013). Stimfit: A fast visualization and analysis environment for cellular neurophysiology. Biomedical Engineering / Biomedizinische Technik. Graz, Austria: De Gruyter.https://doi.org/10.1515/bmt-2013-4181 

Pernia-Andrade, A., Goswami, S., Stickler, Y., Fröbe, U., Schlögl, A., &; Jonas, P. M. (2012). A deconvolution based method with high sensitivity and temporal resolution for detection of spontaneous synaptic currents in vitro and in vivo. Biophysical Journal. Biophysical.https://doi.org/10.1016/j.bpj.2012.08.039 

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Schlögl, A., Vidaurre, C., &; Sander, T. (2011). BioSig: The free and open source software library for biomedical signal processing. Computational Intelligence and Neuroscience. Hindawi Publishing Corporation.https://doi.org/10.1155/2011/935364 

Schlögl, A., Vidaurre, C., &; Müller, K.-R. (2010). Adaptive Methods in BCI Research - An Introductory Tutorial. In B. Graimann, G. Pfurtscheller, &; B. Allison (Eds.), Brain-Computer Interfaces (1st ed., pp. 331–355). Berlin, Heidelberg: Springer.https://doi.org/10.1007/978-3-642-02091-9_18