A structurally precise mechanism links an epilepsy-associated KCN potassium channel mutation to interneuron dysfunction
J. Clatot, C. Currin, Q. Liang, T. Pipatpolkai, S.L. Massey, I. Helbig, L. Delemotte, T.P. Vogels, M. Covarrubias, E.M. Goldberg, Proceedings of the National Academy of Sciences of the United States of America 121 (2024).

Dynamic and selective engrams emerge with memory consolidation
D. Feitosa Tomé, Y. Zhang, T. Aida, O. Mosto, Y. Lu, M. Chen, S. Sadeh, D.S. Roy, C. Clopath, Nature Neuroscience (2024).

Altered childhood brain development in autism and epilepsy
C. Currin, C. Beyer, in:, B. Halpern-Felsher (Ed.), Encyclopedia of Child and Adolescent Health, 1st ed., Elsevier, 2023, pp. 86–98.

douglastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation
D. Feitosa Tomé, (2023).

A framework for grassroots research collaboration in machine learning and global health
C. Currin, M.N. Asiedu , C. Fourie, B. Rosman, H. Turki, A. Lambebo Tonja, J. Abbott, M. Ajala, S.A. Adedayo, C.C. Emezue, D. Machangara, in:, 1st Workshop on Machine Learning &; Global Health, OpenReview, 2023.

Synapseek: Meta-learning synaptic plasticity rules
B.J. Confavreux, Synapseek: Meta-Learning Synaptic Plasticity Rules, Institute of Science and Technology Austria, 2023.

Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species
C. Chintaluri, T.P. Vogels, Proceedings of the National Academy of Sciences of the United States of America 120 (2023).

Depolarization of echo chambers by random dynamical nudge
C. Currin, S.V. Vera, A. Khaledi-Nasab, Scientific Reports 12 (2022).

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

High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating
W.F. Podlaski, E.J. Agnes, T.P. Vogels, BioRxiv (2022).

A familiar thought: Machines that replace us?
B.J. Confavreux, T.P. Vogels, Neuron 110 (2022) 361–362.

Developmentally regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome
K. Kaneko, C. Currin, K.M. Goff, E.R. Wengert, A. Somarowthu, T.P. Vogels, E.M. Goldberg, Cell Reports 38 (2022).

Developmental depression-to-facilitation shift controls excitation-inhibition balance
D.W. Jia, T.P. Vogels, R.P. Costa, Communications Biology 5 (2022).

Regimes and mechanisms of transient amplification in abstract and biological neural networks
G. Christodoulou, T.P. Vogels, E.J. Agnes, PLoS Computational Biology 18 (2022).

The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
F. Zenke, T.P. Vogels, Neural Computation 33 (2021) 899–925.

Talking science, online
P. Bozelos, T.P. Vogels, Nature Reviews Neuroscience 22 (2021) 1–2.

Towards democratizing and automating online conferences: Lessons from the Neuromatch Conferences
T. Achakulvisut, T. Ruangrong, P. Mineault, T.P. Vogels, M.A.K. Peters, P. Poirazi, C. Rozell, B. Wyble, D.F.M. Goodman, K.P. Kording, Trends in Cognitive Sciences 25 (2021) 265–268.

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

Complementary inhibitory weight profiles emerge from plasticity and allow attentional switching of receptive fields
E.J. Agnes, A.I. Luppi, T.P. Vogels, The Journal of Neuroscience 40 (2020) 9634–9649.

Training deep neural density estimators to identify mechanistic models of neural dynamics
P.J. Gonçalves, J.-M. Lueckmann, M. Deistler, M. Nonnenmacher, K. Öcal, G. Bassetto, C. Chintaluri, W.F. Podlaski, S.A. Haddad, T.P. Vogels, D.S. Greenberg, J.H. Macke, ELife 9 (2020).