Publications

Grèzes, J., Adenis, M., Pouga, L. & Armony, J. (2013). Self-relevance modulates brain responses to angry body expressions. Cortex; a journal devoted to the study of the nervous system and behavior, 49(8), 2210-20. doi:10.1016/j.cortex.2012.08.025

Grèzes, J. (2011). Les émotions, modes d’action et de communication. Med Sci (Paris), 27(8-9), 683-684. doi:10.1051/medsci/2011278001

Graupner, M., Wallisch, P. & Ostojic, S. (2019). Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate. Journal of Neuroscience , 36 (44), 11238-11258. doi:10.1523/JNEUROSCI.0104-16.2016

Graupner, M., Maex, R. & Gutkin, B. (2013). Endogenous Cholinergic Inputs and Local Circuit Mechanisms Govern the Phasic Mesolimbic Dopamine Response to Nicotine. PLoS Computational Biology, 9(8). doi:10.1371/journal.pcbi.1003183

Graupner, M., Wallisch, P. & Ostojic, S. (2016). Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate. The Journal of Neuroscience, 36(44), 11238-11258. doi:10.1523/JNEUROSCI.0104-16.2016

Graupner, M. & Gutkin, B. (2009). Modeling nicotinic neuromodulation from global functional and network levels to nAChR based mechanisms. Acta Pharmacologica Sinica, 30(6), 681-693. doi:10.1038/aps.2009.87

Graupner, M., Ahmed, S. & Gutkin, B. (2009). Modelling dynamics of addiction. Pharmacopsychiatry, 42(Suppl 1), S144-52

Giulio, B., Thomas, D., Brice, B. & Ostojic, S. (2021). Network dynamics underlying OFF responses in the auditory cortex. eLife, 10, e53151. doi:10.7554/eLife.53151

Giavazzi, M., Daland, R., Palminteri, S., Peperkamp, S., Brugières, P., Jacquemot, C., Schramm, C., Cleret de Langavant, L. & Bachoud-Levi, A. (2018 ). The role of the striatum in linguistic selection: Evidence from Huntington’s disease and computational modeling. Cortex, 109, 189-204. doi:10.1016/j.cortex.2018.08.031

Garcia, B., Cerrotti, F. & Palminteri, S. (2021). The description-experience gap: a challenge for the neuroeconomics of decision-making under uncertainty. Philosophical transactions of the royal society B, 376(1819). doi:10.1098/rstb.2019.0665

Gamond, L., Vilarem, E., Safra, L., Conty, L. & Grèzes, J. (2017). Minimal group membership biases early neural processing of emotional expressions. The European journal of neuroscience, 46(10), 2584-2595. doi:10.1111/ejn.13735

Gamond, L., Tallon-Baudry, C., Guyon, N., Lemaréchal, J., Hugueville, L. & George, N. (2012). Behavioral evidence for differences in social and non-social category learning. Frontiers in Psychology, 3, 291. doi:10.3389/fpsyg.2012.00291

Fontolan, L., Krupa, M., Hyafil, A. & Gutkin, B. (2013). Analytical insights on theta-gamma coupled neural oscillators. Journal of Mathematical Neuroscience, 3(1), 1-20. doi:10.1186/2190-8567-3-16

Fontanesi , L., Palminteri, S. & Lebreton, M. (2019). Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: A meta-analytical approach using diffusion decision modeling. Cognitive Affective Behavioral Neuroscience, 1-13. doi:10.3758/s13415-019-00723-1

Florence, C., Rebollo, I., Anne, U., Wyart, V. & Tallon-Baudry, C. (2016). Conscious Vision Proceeds from Global to Local Content in Goal-Directed Tasks and Spontaneous Vision. The Journal of Neuroscience, 36(19), 5200-5213. doi:10.1523/JNEUROSCI.3619-15.2016

Findling, C., Chopin, N. & Koechlin, E. (2021). Imprecise neural computations as a source of adaptive behaviour in volatile environments. Nature Human Behaviour , 5, 99-112. doi:10.1038/s41562-020-00971-z

Findling, C. & Wyart, V. (2021). Computation noise in human learning and decision-making: origin, impact, function. Current Opinion in Behavioral Sciences, 38, 124-132. doi:10.1016/j.cobeha.2021.02.018

Findling, C., Skvortsova , V., Dromnelle, R. , Palminteri, S. & Wyart, V. (2019). Computational noise in reward-guided learning drives behavioral variability in volatile environments. Nature Neuroscience , 22, 2066–2077. doi:10.1101/439885

Eskenazi, T., Montalan, B., Jacquot, A., Proust, J., Grèzes, J. & Conty, L. (2016). Social influence on metacognitive evaluations: The power of nonverbal cues. Quarterly journal of experimental psychology (2006), 69(11), 2233-47. doi:10.1080/17470218.2015.1115111

Engelen, T., L. Rademaker , R. & Sack, A. (2019). Reduced Fading of Visual Afterimages after Transcranial Magnetic Stimulation over Early Visual Cortex. , 31(9), 1368-1379. doi:10.1162/jocn_a_01415

Engelen, T., Zhan, M. & de Gelder, B. (2019). Influence of continuous flash suppression mask frequency on stimulus visibility. Neuropsychologia, 128, 65-72. doi:10.1016/j.neuropsychologia.2018.05.012

El Zein, M., Wyart, V. & Grèzes, J. (2015). Anxiety dissociates the adaptive functions of sensory and motor response enhancements to social threats. eLife, 4. doi:10.7554/eLife.10274

El Zein, M., Wyart, V. & Grèzes, J. (2018). Pervasive influence of idiosyncratic associative biases during facial emotion recognition. Scientific Reports, 8(1), 8804. doi:10.1038/s41598-018-27102-z

El Zein, M., Gamond, L., Conty, L. & Grèzes, J. (2015). Selective attention effects on early integration of social signals: same timing, modulated neural sources. NeuroImage, 106, 182-8. doi:10.1016/j.neuroimage.2014.10.063

Duverne, S. & Koechlin, E. (2017). Rewards and Cognitive Control in the Human Prefrontal Cortex. Cerebral cortex (New York, N.Y. : 1991), 27(10), 5024-5039. doi:10.1093/cercor/bhx210

Dumont, G., Ermentrout, G. & Gutkin, B. (2017). Macroscopic phase-resetting curves for spiking neural networks. Physical review. E, 96, 042311. doi:10.1103/PhysRevE.96.042311

Dumont, G., Payeur , A. & Longtin , A. (2017). A stochastic-field description of finite-size spiking neural networks. PLoS Comput Biol, 13(8), e1005691. doi:10.1371/journal.pcbi.1005691

Dubreuil, A. (2019). Short term memory properties of sensory neural architectures. Journal of Computational Neuroscience volume, 46, 321–332. doi:10.1007/s10827-019-00720-w

Domenech, P., Rheims, S. & Koechlin, E. (2020 ). Neural Mechanisms resolving Exploitation-Exploration dilemmas in the prefrontal cortex. Science, 369(6507), eabb0184. doi:10.1126/science.abb0184

Dipoppa, M. & Gutkin, B. (2013). Flexible frequency control of cortical oscillations enables computations required for working memory. Proceedings of the National Academy of Sciences of the United States of America, 110(31), 12828-12833. doi:10.1073/pnas.1303270110