J. Clatot et al., “A structurally precise mechanism links an epilepsy-associated KCN potassium channel mutation to interneuron dysfunction,” Proceedings of the National Academy of Sciences of the United States of America, vol. 121, no. 3. Proceedings of the National Academy of Sciences, 2024.

D. Feitosa Tomé et al., “Dynamic and selective engrams emerge with memory consolidation,” Nature Neuroscience. Springer Nature, 2024.

E. J. Agnes and T. P. Vogels, “Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks,” Nature Neuroscience. Springer Nature, 2024.

C. Chintaluri et al., “kCSD-python, reliable current source density estimation with quality control,” PLoS Computational Biology, vol. 20, no. 3. Public Library of Science, 2024.

S. M. Hall et al., “Ten simple rules for pushing boundaries of inclusion at academic events,” PLOS Computational Biology, vol. 20, no. 3. Public Library of Science, 2024.

C. Beyer, C. Currin, T. Williams, and D. J. Stein, “Meta-analysis of the comparative efficacy of benzodiazepines and antidepressants for psychic versus somatic symptoms of generalized anxiety disorder,” Comprehensive Psychiatry, vol. 132. Elsevier.

G. Delamare, D. Feitosa Tomé, and C. Clopath, “Intrinsic neural excitability biases allocation and overlap of memory engrams,” Journal of Neuroscience, vol. 44, no. 21. Society of Neuroscience, 2024.

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

B. J. Confavreux, “Synapseek: Meta-learning synaptic plasticity rules,” Institute of Science and Technology Austria, 2023.

C. Chintaluri and T. P. Vogels, “Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species,” Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 48. National Academy of Sciences, 2023.

D. Feitosa Tomé, glastome/dynamic-engrams: Dynamic and selective engrams emerge with memory consolidation.” Zenodo, 2023.

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.

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.