C. Currin et al., “A framework for grassroots research collaboration in machine learning and global health,” in 1st Workshop on Machine Learning &; Global Health, Kigali, Rwanda, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14993

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.
LibreCat: https://research-explorer.ista.ac.at/record/13239

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.
LibreCat: https://research-explorer.ista.ac.at/record/11143
DOI: 10.1016/j.celrep.2022.110580
WoS: 000779794000001

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.
LibreCat: https://research-explorer.ista.ac.at/record/12009
DOI: 10.1038/s42003-022-03801-2
WoS: 000844814800007

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.
LibreCat: https://research-explorer.ista.ac.at/record/12084
DOI: 10.1371/journal.pcbi.1010365
WoS: 000937227700001

C. Currin, S. V. Vera, and A. Khaledi-Nasab, “Depolarization of echo chambers by random dynamical nudge,” Scientific Reports, vol. 12. Springer Nature, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/12225
DOI: 10.1038/s41598-022-12494-w
WoS: 000805561200024
PMID: 35654942

B. J. Confavreux and T. P. Vogels, “A familiar thought: Machines that replace us?,” Neuron, vol. 110, no. 3. Elsevier, pp. 361–362, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/10753
DOI: 10.1016/j.neuron.2022.01.014
WoS: 000751819100005
PMID: 35114107

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.
LibreCat: https://research-explorer.ista.ac.at/record/8125
DOI: 10.1101/2020.01.08.898528

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.
LibreCat: https://research-explorer.ista.ac.at/record/11453

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.
LibreCat: https://research-explorer.ista.ac.at/record/8253
DOI: 10.1162/neco_a_01367
WoS: 000663433900003
PMID: 33513328

P. Bozelos and T. P. Vogels, “Talking science, online,” Nature Reviews Neuroscience, vol. 22, no. 1. Springer Nature, pp. 1–2, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/8757
DOI: 10.1038/s41583-020-00408-6
WoS: 000588256300001
PMID: 33173190

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.
LibreCat: https://research-explorer.ista.ac.at/record/9228
DOI: 10.1016/j.tics.2021.01.007
WoS: 000627418000001
PMID: 33608214

P. J. Gonçalves et al., “Training deep neural density estimators to identify mechanistic models of neural dynamics,” eLife, vol. 9. eLife Sciences Publications, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/8127
DOI: 10.7554/eLife.56261
WoS: 000584989400001
PMID: 32940606

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.
LibreCat: https://research-explorer.ista.ac.at/record/8126
DOI: 10.1523/JNEUROSCI.0276-20.2020
WoS: 000606706400009
PMID: 33168622

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.
LibreCat: https://research-explorer.ista.ac.at/record/9633