Feitosa Tomé, D. (2023). douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation. Zenodo.https://doi.org/10.5281/ZENODO.10251087 
LibreCat: https://research-explorer.ista.ac.at/record/14892
DOI: 10.5281/ZENODO.10251087

Currin, C., Asiedu , M. N., Fourie, C., Rosman, B., Turki, H., Lambebo Tonja, A.,  Machangara, D. (2023). A framework for grassroots research collaboration in machine learning and global health. In 1st Workshop on Machine Learning &; Global Health. Kigali, Rwanda: OpenReview.
LibreCat: https://research-explorer.ista.ac.at/record/14993

Van Der Plas, T. L., Vogels, T. P., &; Manohar, S. G. (2022). Predictive learning enables neural networks to learn complex working memory tasks. In Proceedings of Machine Learning Research (Vol. 199, pp. 518–531). ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/13239

Kaneko, K., Currin, C., Goff, K. M., Wengert, E. R., Somarowthu, A., Vogels, T. P., &; Goldberg, E. M. (2022). Developmentally regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome. Cell Reports. Elsevier.https://doi.org/10.1016/j.celrep.2022.110580 
LibreCat: https://research-explorer.ista.ac.at/record/11143
DOI: 10.1016/j.celrep.2022.110580
WoS: 000779794000001

Jia, D. W., Vogels, T. P., &; Costa, R. P. (2022). Developmental depression-to-facilitation shift controls excitation-inhibition balance. Communications Biology. Springer Nature.https://doi.org/10.1038/s42003-022-03801-2 
LibreCat: https://research-explorer.ista.ac.at/record/12009
DOI: 10.1038/s42003-022-03801-2
WoS: 000844814800007

Christodoulou, G., Vogels, T. P., &; Agnes, E. J. (2022). Regimes and mechanisms of transient amplification in abstract and biological neural networks. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1010365 
LibreCat: https://research-explorer.ista.ac.at/record/12084
DOI: 10.1371/journal.pcbi.1010365
WoS: 000937227700001

Currin, C., Vera, S. V., &; Khaledi-Nasab, A. (2022). Depolarization of echo chambers by random dynamical nudge. Scientific Reports. Springer Nature.https://doi.org/10.1038/s41598-022-12494-w 
LibreCat: https://research-explorer.ista.ac.at/record/12225
DOI: 10.1038/s41598-022-12494-w
WoS: 000805561200024
PMID: 35654942

Confavreux, B. J., &; Vogels, T. P. (2022). A familiar thought: Machines that replace us? Neuron. Elsevier.https://doi.org/10.1016/j.neuron.2022.01.014 
LibreCat: https://research-explorer.ista.ac.at/record/10753
DOI: 10.1016/j.neuron.2022.01.014
WoS: 000751819100005
PMID: 35114107

Podlaski, W. F., Agnes, E. J., &; Vogels, T. P. (2022). High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating. bioRxiv. Cold Spring Harbor Laboratory.https://doi.org/10.1101/2020.01.08.898528 
LibreCat: https://research-explorer.ista.ac.at/record/8125
DOI: 10.1101/2020.01.08.898528

Braun, L., &; Vogels, T. P. (2021). Online learning of neural computations from sparse temporal feedback. In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems (Vol. 20, pp. 16437–16450). Virtual, Online: Neural Information Processing Systems Foundation.
LibreCat: https://research-explorer.ista.ac.at/record/11453

Zenke, F., &; Vogels, T. P. (2021). The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. Neural Computation. MIT Press.https://doi.org/10.1162/neco_a_01367 
LibreCat: https://research-explorer.ista.ac.at/record/8253
DOI: 10.1162/neco_a_01367
WoS: 000663433900003
PMID: 33513328

Bozelos, P., &; Vogels, T. P. (2021). Talking science, online. Nature Reviews Neuroscience. Springer Nature.https://doi.org/10.1038/s41583-020-00408-6 
LibreCat: https://research-explorer.ista.ac.at/record/8757
DOI: 10.1038/s41583-020-00408-6
WoS: 000588256300001
PMID: 33173190

Achakulvisut, T., Ruangrong, T., Mineault, P., Vogels, T. P., Peters, M. A. K., Poirazi, P.,https://doi.org/10.1016/j.tics.2021.01.007__Kording, K. P. (2021). Towards democratizing and automating online conferences: Lessons from the Neuromatch Conferences. Trends in Cognitive Sciences. Elsevier.https://doi.org/10.1016/j.tics.2021.01.007 
LibreCat: https://research-explorer.ista.ac.at/record/9228
DOI: 10.1016/j.tics.2021.01.007
WoS: 000627418000001
PMID: 33608214

Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G.,https://doi.org/10.7554/eLife.56261__Macke, J. H. (2020). Training deep neural density estimators to identify mechanistic models of neural dynamics. ELife. eLife Sciences Publications.https://doi.org/10.7554/eLife.56261 
LibreCat: https://research-explorer.ista.ac.at/record/8127
DOI: 10.7554/eLife.56261
WoS: 000584989400001
PMID: 32940606

Agnes, E. J., Luppi, A. I., &; Vogels, T. P. (2020). Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields. The Journal of Neuroscience. Society for Neuroscience.https://doi.org/10.1523/JNEUROSCI.0276-20.2020 
LibreCat: https://research-explorer.ista.ac.at/record/8126
DOI: 10.1523/JNEUROSCI.0276-20.2020
WoS: 000606706400009
PMID: 33168622

Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., &; Vogels, T. P. (2020). A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In Advances in Neural Information Processing Systems (Vol. 33, pp. 16398–16408). Vancouver, Canada.
LibreCat: https://research-explorer.ista.ac.at/record/9633