Dr. Everton Joao Agnes

Confavreux, Basile, Agnes, Everton J., Zenke, Friedemann, Sprekeler, Henning, & Vogels, Tim P. (2024). Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks [Posted-content]. In bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2024.06.17.599260

Agnes, Everton J., & Vogels, Tim P. (2024). Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks. Nature Neuroscience, 27(5), 964–974. https://doi.org/10.1038/s41593-024-01597-4

Christodoulou, Georgia, Vogels, Tim P., & Agnes, Everton J. (2022). Regimes and mechanisms of transient amplification in abstract and biological neural networks. PLoS Computational Biology, 18(8), e1010365. https://doi.org/10.1371/journal.pcbi.1010365

Agnes, Everton J., Luppi, Andrea I., & Vogels, Tim P. (2020). Complementary Inhibitory Weight Profiles Emerge from Plasticity and Allow Flexible Switching of Receptive Fields. Journal of Neuroscience, 40(50), 9634–9649. https://doi.org/10.1523/jneurosci.0276-20.2020

Confavreux, Basile, Zenke, Friedemann, Agnes, Everton J., Lillicrap, Timothy, & Vogels, Tim P. (2020). A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network (Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M. F.; Lin, H., Ed.). Curran Associates, Inc. https://doi.org/10.1101/2020.10.24.353409

Podlaski, William F., Agnes, Everton J., & Vogels, Tim P. (2020). Context-modular memory networks support high-capacity, flexible, and robust associative memories. bioRxiv. https://doi.org/10.1101/2020.01.08.898528

Hennequin, Guillaume, Agnes, Everton J., & Vogels, Tim P. (2017). Inhibitory plasticity: Balance, control, and codependence. Annual Review of Neuroscience, 40, 557–579. https://doi.org/10.1146/annurev-neuro-072116-031005

Mizusaki, Beatriz E. P., Agnes, Everton J., Erichsen Jr, Rubem, & Brunnet, Leonardo G. (2017). Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity. Physica. A, Theoretical and Statistical Physics, 479, 279–286. https://doi.org/10.1016/j.physa.2017.02.035

Zenke, Friedemann, Agnes, Everton J., & Gerstner, Wulfram. (2015). Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nature Communications, 6, 6922. https://doi.org/10.1038/ncomms7922

Agnes, Everton J., Mizusaki, Beatriz E. P., Erichsen Jr, Rubem, & Brunnet, Leonardo G. (2013). Strategies to associate memories by unsupervised learning in neural networks. AIP Conference Proceedings, 1510(1), 255–257. https://doi.org/10.1063/1.4776533

Brunnet, Leonardo G., Agnes, Everton J., Mizusaki, Beatriz E. P., & Erichsen Jr, Rubem. (2013). Unsupervised learning in neural networks with short range synapses. AIP Conference Proceedings, 1510(1), 251–254. https://doi.org/10.1063/1.4776532

Mizusaki, Beatriz E. P., Agnes, Everton J., Brunnet, Leonardo G., & Erichsen Jr, Rubem. (2013). Spike timing analysis in neural networks with unsupervised synaptic plasticity. AIP Conference Proceedings, 1510(1), 213–215. https://doi.org/10.1063/1.4776522

Agnes, Everton J., Erichsen Jr, Rubem, & Brunnet, Leonardo G. (2012). Model architecture for associative memory in a neural network of spiking neurons. Physica. A, Theoretical and Statistical Physics, 391(3), 843–848. https://doi.org/10.1016/j.physa.2011.08.036

Agnes, Everton J., Erichsen Jr, Rubem, & Brunnet, Leonardo G. (2012). Associative memory in neuronal networks of spiking neurons: architecture and storage analysis. In Villa, Alessandro E. P.; Duch, Włodzisław; Érdi, Péter; Masulli, Francesco; Palm, Günther (Ed.), Lecture Notes in Computer Science. Springer. https://doi.org/10.1007/978-3-642-33269-2_19

Agnes, Everton J., Erichsen Jr, Rubem, & Brunnet, Leonardo G. (2010). Synchronization regimes in a map-based model neural network. Physica. A, Theoretical and Statistical Physics, 389(3), 651–658. https://doi.org/10.1016/j.physa.2009.10.012