Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action
D. D’Elia, J. Truu, L. Lahti, M. Berland, G. Papoutsoglou, M. Ceci, A. Zomer, M.B. Lopes, E. Ibrahimi, A. Gruca, A. Nechyporenko, M. Frohme, T. Klammsteiner, E.C.D.S. Pau, L.J. Marcos-Zambrano, K. Hron, G. Pio, A. Simeon, R. Suharoschi, I. Moreno-Indias, A. Temko, M. Nedyalkova, E.S. Apostol, C.O. Truic, R. Shigdel, J.H. Telalovi, E. Bongcam-Rudloff, P. Przymus, N.B. Jordamovi, L. Falquet, S. Tarazona, A. Sampri, G. Isola, D. Pérez-Serrano, V. Trajkovik, L. Klucar, T. Loncar-Turukalo, A.S. Havulinna, C. Jansen, R.J. Bertelsen, M.J. Claesson, Frontiers in Microbiology 14 (2023).

Where is the sweet spot? A procurement story of general purpose compute nodes
A. Schlögl, A. Hornoiu, S. Elefante, S. Stadlbauer, in:, ASHP - Austrian-Slovenian HPC Meeting 2022, EuroCC Austria c/o Universität Wien, 2022, p. 7.

Managing software on a heterogenous HPC cluster
A. Schlögl, S. Elefante, A. Hornoiu, S. Stadlbauer, in:, ASHP – Austrian-Slovenian HPC Meeting 2021, University of Ljubljana, 2021, p. 5.

MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo
X. Zhang, A. Schlögl, D.H. Vandael, P.M. Jonas, Journal of Neuroscience Methods 357 (2021).

The history and challenges of SCP-ECG: The standard communication protocol for computer-assisted electrocardiography
P. Rubel, J. Fayn, P.W. Macfarlane, D. Pani, A. Schlögl, A. Värri, Hearts 2 (2021) 384–409.

How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–C network
J. Guzmán, A. Schlögl, C. Espinoza Martinez, X. Zhang, B. Suter, P.M. Jonas, (2021).

Austrian High-Performance-Computing meeting (AH)
A. Schlögl, J. Kiss, S. Elefante, eds., Austrian High-Performance-Computing Meeting (AH), IST Austria, Klosterneuburg, Austria, 2020.

Selective routing of spatial information flow from input to output in hippocampal granule cells
X. Zhang, A. Schlögl, P.M. Jonas, Neuron 107 (2020) 1212–1225.

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

Biosignals standards and FHIR: The way to go
S. Sauermann, V. David, A. Schlögl, R. Egelkraut, M. Frohner, B. Pohn, P. Urbauer, A. Mense, in:, IOS Press, 2017, pp. 356–362.

Scientific Computing at IST Austria
A. Schlögl, J. Kiss, in:, AHP – Austrian HPC Meeting 2017, FSP Scientific Computing, 2017, p. 28.

Synaptic mechanisms of pattern completion in the hippocampal C network
J. Guzmán, A. Schlögl, M. Frotscher, P.M. Jonas, Science 353 (2016) 1117–1123.

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

Sequential effects in continued visual search: Using fixation-related potentials to compare distractor processing before and after target detection
C. Körner, V. Braunstein, M. Stangl, A. Schlögl, C. Neuper, A. Ischebeck, Psychophysiology 51 (2014) 385–395.

Stimfit: Quantifying electrophysiological data with Python
J. Guzmán, A. Schlögl, C. Schmidt Hieber, Frontiers in Neuroinformatics 8 (2014).

A deconvolution based method with high sensitivity and temporal resolution for detection of spontaneous synaptic currents in vitro and in vivo
A. Pernia-Andrade, S. Goswami, Y. Stickler, U. Fröbe, A. Schlögl, P.M. Jonas, Biophysical Journal 103 (2012) 1429–1439.

Review of the BCI competition IV
M. Tangermann, K. Müller, A. Aertsen, N. Birbaumer, C. Braun, C. Brunner, R. Leeb, C. Mehring, K. Miller, G. Müller Putz, G. Nolte, G. Pfurtscheller, H. Preissl, G. Schalk, A. Schlögl, C. Vidaurre, S. Waldert, B. Blankertz, Frontiers in Neuroscience 6 (2012).

BioSig: The free and open source software library for biomedical signal processing
A. Schlögl, C. Vidaurre, T. Sander, Computational Intelligence and Neuroscience 2011 (2011).

Adaptive Methods in BCI Research - An Introductory Tutorial
A. Schlögl, C. Vidaurre, K.-R. Müller, in:, B. Graimann, G. Pfurtscheller, B. Allison (Eds.), Brain-Computer Interfaces, 1st ed., Springer, Berlin, Heidelberg, 2010, pp. 331–355.