Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks
B.J. Confavreux, E.J. Agnes, F. Zenke, H. Sprekeler, T.P. Vogels, PLoS Computational Biology 21 (2025).

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, Physical Review X 15 (2025).

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).

Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks
E.J. Agnes, T.P. Vogels, Nature Neuroscience 27 (2024) 964–974.

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).

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

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

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).

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).

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.

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.

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).

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.

Think: Theory for Africa
C.B. Currin, P.N. Khoza, A.D. Antrobus, P.E. Latham, T.P. Vogels, J.V. Raimondo, PLOS Computational Biology 15 (2019).

Neural mechanisms of attending to items in working memory
S.G. Manohar, N. Zokaei, S.J. Fallon, T.P. Vogels, M. Husain, Neuroscience and Biobehavioral Reviews 101 (2019) 1–12.

Cortical signal propagation: Balance, amplify, transmit
J.P. Stroud, T.P. Vogels, Neuron 98 (2018) 8–9.