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).

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

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.

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).

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.

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).

A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network
B.J. Confavreux, F. Zenke, E.J. Agnes, T. Lillicrap, T.P. Vogels, in:, Advances in Neural Information Processing Systems, 2020, pp. 16398–16408.