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T. L. Van Der Plas, T. P. Vogels, and S. G. Manohar, “Predictive learning enables neural networks to learn complex working memory tasks,” in Proceedings of Machine Learning Research, 2022, vol. 199, pp. 518–531.

K. Kaneko et al., “Developmentally regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome,” Cell Reports, vol. 38, no. 13. Elsevier, 2022.

D. W. Jia, T. P. Vogels, and R. P. Costa, “Developmental depression-to-facilitation shift controls excitation-inhibition balance,” Communications biology, vol. 5. Springer Nature, 2022.

G. Christodoulou, T. P. Vogels, and E. J. Agnes, “Regimes and mechanisms of transient amplification in abstract and biological neural networks,” PLoS Computational Biology, vol. 18, no. 8. Public Library of Science, 2022.

C. Currin, S. V. Vera, and A. Khaledi-Nasab, “Depolarization of echo chambers by random dynamical nudge,” Scientific Reports, vol. 12. Springer Nature, 2022.

B. J. Confavreux and T. P. Vogels, “A familiar thought: Machines that replace us?,” Neuron, vol. 110, no. 3. Elsevier, pp. 361–362, 2022.

W. F. Podlaski, E. J. Agnes, and T. P. Vogels, “High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating,” bioRxiv. Cold Spring Harbor Laboratory, 2022.

L. Braun and T. P. Vogels, “Online learning of neural computations from sparse temporal feedback,” in Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 20, pp. 16437–16450.

F. Zenke and T. P. Vogels, “The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks,” Neural Computation, vol. 33, no. 4. MIT Press, pp. 899–925, 2021.

P. Bozelos and T. P. Vogels, “Talking science, online,” Nature Reviews Neuroscience, vol. 22, no. 1. Springer Nature, pp. 1–2, 2021.

T. Achakulvisut et al., “Towards democratizing and automating online conferences: Lessons from the Neuromatch Conferences,” Trends in Cognitive Sciences, vol. 25, no. 4. Elsevier, pp. 265–268, 2021.

P. J. Gonçalves et al., “Training deep neural density estimators to identify mechanistic models of neural dynamics,” eLife, vol. 9. eLife Sciences Publications, 2020.

E. J. Agnes, A. I. Luppi, and T. P. Vogels, “Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields,” The Journal of Neuroscience, vol. 40, no. 50. Society for Neuroscience, pp. 9634–9649, 2020.

B. J. Confavreux, F. Zenke, E. J. Agnes, T. Lillicrap, and T. P. Vogels, “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, Vancouver, Canada, 2020, vol. 33, pp. 16398–16408.