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PMID: 38564872

Delamare, G., Feitosa Tomé, D., &; Clopath, C. (2024). Intrinsic neural excitability biases allocation and overlap of memory engrams. Journal of Neuroscience. Society of Neuroscience.https://doi.org/10.1523/JNEUROSCI.0846-23.2024 
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PMID: 38561228

Currin, C., &; Beyer, C. (2023). Altered childhood brain development in autism and epilepsy. In B. Halpern-Felsher (Ed.), Encyclopedia of Child and Adolescent Health (1st ed., pp. 86–98). Elsevier.https://doi.org/10.1016/b978-0-12-818872-9.00129-1 
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Confavreux, B. J. (2023). Synapseek: Meta-learning synaptic plasticity rules. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:14422 
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Chintaluri, C., &; Vogels, T. P. (2023). Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences.https://doi.org/10.1073/pnas.2306525120 
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Feitosa Tomé, D. (2023). douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation. Zenodo.https://doi.org/10.5281/ZENODO.10251087 
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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.
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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 
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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
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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
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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 
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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 
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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 
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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.
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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 
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Bozelos, P., &; Vogels, T. P. (2021). Talking science, online. Nature Reviews Neuroscience. Springer Nature.https://doi.org/10.1038/s41583-020-00408-6 
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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 
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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 
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DOI: 10.7554/eLife.56261
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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