Publikationen von Viola Priesemann

Wollstadt, P.; Rathbun, D.L.; Usrey, W.M.; Bastos, A.M.; Lindner, M.; Priesemann, V.; Wibral, M.: Information-theoretic analyses of neural data to minimize the effect of researchers’ assumptions in predictive coding studies. PLoS Computational Biology 19 (11), e1011567 (2023)

Contreras, S. A.; Iftekhar, E. N.; Priesemann, V.: From emergency response to long-term management: the many faces of the endemic state of COVID-19. The Lancet Regional Health-Europe 30, 100664 (2023)

Contreras, S. A.; Oróstica, K. Y.; Daza-Sanchez, A.; Wagner, J.; Dönges, P.; Medina-Ortiz, D.; Jara, M.; Verdugo, R.; Conca, C.; Priesemann, V. et al.; Olivera-Nappa, Á.: Model-based assessment of sampling protocols for infectious disease genomic surveillance. Chaos, Solitons & Fractals 167, 113093 (2023)

Cramer, B.; Kreft, M.; Billaudelle, S.; Karasenko, V.; Leibfried, A.; Müller, E.; Spilger, P.; Weis, J.; Schemmel, J.; Muñoz, M. A. et al.; Priesemann, V.; Zierenberg, J.: Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware. Physical Review Research 5 (3), 033035 (2023)

Davenport, F.; Gallacher, J.; Kourtzi, Z.; Koychev, I.; Matthews, P. M.; Oxtoby, N. P.; Parkes, L. M.; Priesemann, V.; Rowe, J. B.; Smye, S. W. et al.; Zetterberg, H.: Neurodegenerative disease of the brain: a survey of interdisciplinary approaches. Journal of the Royal Society Interface 20 (198), 20220406 (2023)

Dehning, J.; Mohr, S. B.; Contreras, S.; Dönges, P.; Iftekhar, E. N.; Schulz, O.; Bechtle, P.; Priesemann, V.: Impact of the Euro 2020 championship on the spread of COVID-19. Nature Communications 14, 122 (2023)

Ehrlich, D. A.; Schneider, A. C.; Priesemann, V.; Wibral, M.; Makkeh, A.: A Measure of the Complexity of Neural Representations based on Partial Information Decomposition. Transactions on Machine Learning Research 05/2023 (2023)

Kekić, A.; Dehning, J.; Gresele, L.; von Kügelgen, J.; Priesemann, V.; Schölkopf, B.: Evaluating vaccine allocation strategies using simulation-assisted causal modeling. Patterns 4, 100739 (2023)

Mikulasch, F.; Rudelt, L.; Wibral, M.; Priesemann, V.: Where is the error? Hierarchical predictive coding through dendritic error computation. Trends in Neurosciences 46 (1), S. 45 - 59 (2023)

Rowland, J.M.; van der Plas, T.L.; Loidolt, M.; Lees, R.M.; Keeling, J.; Dehning, J.; Akam, T.; Priesemann, V.; Packer, A.M.: Propagation of activity through the cortical hierarchy and perception are determined by neural variability. Nature Neuroscience 26 (9), S. 1584 - 1594 (2023)

Sherratt, K.; Gruson, H.; Grah, R.; Johnson, H.; Niehus, R.; Prasse, B.; Sandmann, F.; Deuschel, J.; Wolffram, D.; Abbott, S. et al.; Ullrich, A.; Gibson, G.; Ray, E. L.; Reich, N. G.; Sheldon, D.; Wang, Y.; Wattanachit, N.; Wang, L.; Trnka, J.; Obozinski, G.; Sun, T.; Thanou, D.; Pottier, L.; Krymova, E.; Meinke, J. H.; Barbarossa, M. V.; Leithauser, N.; Mohring, J.; Schneider, J.; Wlazlo, J.; Fuhrmann, J.; Lange, B.; Rodiah, I.; Baccam, P.; Gurung, H.; Stage, S.; Suchoski, B.; Budzinski, J.; Walraven, R.; Villanueva, I.; Tucek, V.; Smid, M.; Zajicek, M.; Perez Alvarez, C.; Reina, B.; Bosse, N. I.; Meakin, S. R.; Castro, L.; Fairchild, G.; Michaud, I.; Osthus, D.; Alaimo Di Loro, P.; Maruotti, A.; Eclerova, V.; Kraus, A.; Kraus, D.; Pribylova, L.; Dimitris, B.; Li, M. L.; Saksham, S.; Dehning, J.; Mohr, S. B.; Priesemann, V.; Redlarski, G.; Bejar, B.; Ardenghi, G.; Parolini, N.; Ziarelli, G.; Bock, W.; Heyder, S.; Hotz, T.; Singh, D. E.; Guzman-Merino, M.; Aznarte, J. L.; Morina, D.; Alonso, S.; Alvarez, E.; Lopez, D.; Prats, C.; Burgard, J. P.; Rodloff, A.; Zimmermann, T.; Kuhlmann, A.; Zibert, J.; Pennoni, F.; Divino, F.; Catala, M.; Lovison, G.; Giudici, P.; Tarantino, B.; Bartolucci, F.; Jona Lasinio, G.; Mingione, M.; Farcomeni, A.; Srivastava, A.; Montero-Manso, P.; Adiga, A.; Hurt, B.; Lewis, B.; Marathe, M.; Porebski, P.; Venkatramanan, S.; Bartczuk, R. P.; Dreger, F.; Gambin, A.; Gogolewski, K.; Gruziel-Slomka, M.; Krupa, B.; Moszyński, A.; Niedzielewski, K.; Nowosielski, J.; Radwan, M.; Rakowski, F.; Semeniuk, M.; Szczurek, E.; Zielinski, J.; Kisielewski, J.; Pabjan, B.; Holger, K.; Kheifetz, Y.; Scholz, M.; Przemyslaw, B.; Bodych, M.; Filinski, M.; Idzikowski, R.; Krueger, T.; Ozanski, T.; Bracher, J.; Funk, S.: Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. eLife 12, e81916 (2023)

Yamamoto, H.; Spitzner, F. P.; Takemuro, T.; Buendía, V.; Murota, H.; Morante, C.; Konno, T.; Sato, S.; Hirano-Iwata, A.; Levina, A. et al.; Priesemann, V.; Muñoz, M. A.; Zierenberg, J.; Soriano, J.: Modular architecture facilitates noise-driven control of synchrony in neuronal networks. Science Advances 9 (34), eade175 (2023)

Zierenberg, J.; Spitzner, F. P.; Dehning, J.; Priesemann, V.; Weigel, M.; Wilczek, M.: How contact patterns destabilize and modulate epidemic outbreaks. New Journal of Physics 25, 053033 (2023)

Pinheiro Neto, J.; Spitzner, F. P.; Priesemann, V.: Sampling effects and measurement overlap can bias the inference of neuronal avalanches. PLoS Computational Biology 18 (11), e1010678 (2022)

Contreras, S.; Dehning, J.; Priesemann, V.: Describing a landscape we are yet discovering. AStA Advances in Statistical Analysis (2022)

Contreras, S.; Olivera-Nappa, Á.; Priesemann, V.: Rethinking COVID-19 vaccine allocation: it is time to care about our neighbours. The Lancet Regional Health - Europe 12, 100277 (2022)

Czypionka, T.; Iftekhar, E. N.; Prainsack, B.; Priesemann, V.; Bauer, S.; Calero Valdez, A.; Cuschieri, S.; Glaab, E.; Grill, E.; Krutzinna, J. et al.; Lionis, C.; Machado, H.; Martins, C.; Pavlakis, G. N.; Perc, M.; Petelos, E.; Pickersgill, M.; Skupin, A.; Schernhammer, E.; Szczurek, E.; Tsiodras, S.; Willeit, P.; Wilmes, P.: The benefits, costs and feasibility of a low incidence COVID-19 strategy. The Lancet Regional Health - Europe 13, 100294 (2022)

Dönges, P.; Wagner, J.; Contreras, S.; Iftekhar, E. N.; Bauer, S.; Mohr, S. B.; Dehning, J.; Calero Valdez, A.; Kretzschmar, M.; Mäs, M. et al.; Nagel, K.; Priesemann, V.: Interplay Between Risk Perception, Behavior, and COVID-19 Spread. Frontiers in Physics 10, 842180 (2022)

Leite, K.; Garg, P.; Spitzner, F. P.; Guerin Darvas, S.; Bähr, M.; Priesemann, V.; Kügler, S.: α-Synuclein Impacts on Intrinsic Neuronal Network Activity Through Reduced Levels of Cyclic AMP and Diminished Numbers of Active Presynaptic Terminals. Frontiers in Molecular Neuroscience 15, 868790 (2022)

Levina, A.; Priesemann, V.; Zierenberg, J.: Tackling the subsampling problem to infer collective properties from limited data. Nature Reviews Physics 4, S. 770 - 784 (2022)