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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.

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 

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 

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 

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 

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 

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 

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.

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 

Bozelos, P., &; Vogels, T. P. (2021). Talking science, online. Nature Reviews Neuroscience. Springer Nature.https://doi.org/10.1038/s41583-020-00408-6 

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 

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 

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 

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.