Publications

Engelen, T., Buot, A., Grèzes, J. & Tallon-Baudry, C. (2023). Whose emotion is it? Perspective matters to understand brain-body interactions in emotions. NeuroImage, 268, 119867. doi:10.1016/j.neuroimage.2023.119867

Ladenbauer, J. & Obermayer, K. (2019). Weak electric fields promote resonance in neuronal spiking activity: Analytical results from two-compartment cell and network models. PLOS Computational Biology, 15(4), e1006974. doi:10.1371/journal.pcbi.1006974

Azzalini, D., Rebollo, I. & Tallon-Baudry, C. (2019). Visceral Signals Shape Brain Dynamics and Cognition. Trends in Cognitive Sciences , 23(6), 488-509. doi:10.1016/j.tics.2019.03.007

Ostojic, S., Vlugt, T. & Nienhuis, B. (2007). Universal anisotropy in force networks under shear. Physical Review E, 75(3 Pt 1), 030301. doi:10.1103/PhysRevE.75.030301

Ostojic, S. (2014). Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons. Nature Neuroscience, 17(4), 594-600. doi:10.1038/nn.3658

Pichon, S., De Gelder, B. & Grèzes, J. (2009). Two different faces of threat. Comparing the neural systems for recognizing fear and anger in dynamic body expressions. NeuroImage, 47(4), 1873-83. doi:10.1016/j.neuroimage.2009.03.084

Gutkin, B., Jost, J. & Tuckwell, H. (2008). Transient termination of spiking by noise in coupled neurons. EPL, 81(2). doi:10.1209/0295-5075/81/20005

Safra, L., Chevallier, C., Grèzes, J. & Baumard, N. (2020). Tracking historical changes in trustworthiness using machine learning analyses of facial cues in painting. Nature Communications, 11, 4728. doi:10.1038/s41467-020-18566-7

Stein, H., Barbosa, J. & Compte, A. (2021). Towards biologically constrained attractor models of schizophrenia. Current Opinion in Neurobiology, 70, 171-181. doi:10.1016/j.conb.2021.10.013

Jones, B. , DeBruine, L. , .., .., Chevallier, C. & (Et Al), . (2021). To which world regions does the valence–dominance model of social perception apply? Nat Hum Behav, 5, 159-169. doi:10.6084/m9.figshare.7611443.v1

Collins, T. & Jacquet, P. (2018). TMS over posterior parietal cortex disrupts trans-saccadic visual stability. Brain stimulation, 11(2), 390-399. doi:10.1016/j.brs.2017.11.019

Blot, A., De Solages, C., Ostojic, S., Szapiro, G., Hakim, V. & Léna, C. (2016). Time-invariant feed-forward inhibition of Purkinje cells in the cerebellar cortex in vivo. The Journal of physiology, 594(10), 2729-49. doi:10.1113/JP271518

Pichon, S., De Gelder, B. & Grèzes, J. (2012). Threat prompts defensive brain responses independently of attentional control. Cerebral cortex (New York, N.Y. : 1991), 22(2), 274-85. doi:10.1093/cercor/bhr060

Mennella, R., Sarlo, M., Messerotti Benvenuti, S., Buodo, G., Mento, G. & Palomba, D. (2017). The two faces of avoidance: Time-frequency correlates of motivational disposition in blood phobia. Psychophysiology, 54(11), 1606-1620. doi:10.1111/psyp.12904

Tallon-Baudry, C. (2022). The topological space of subjective experience. Trends in Cognitive Sciences. doi:10.1016/j.tics.2022.09.002

Rebollo, I. & Tallon-Baudry, C. (2022). The sensory and motor components of the cortical hierarchy are coupled to the rhythm of the stomach during rest. Journal of Neuroscience, 42(11), 2205-2220. doi:10.1523/JNEUROSCI.1285-21.2021

Dubreuil, A., Valente, A., Beiran, M., Mastrogiuseppe, F. & Ostojic, S. (2022). The role of population structure in computations through neural dynamics. Nature Neuroscience, 25, 783–794. doi:10.1038/s41593-022-01088-4

Remme, M., Lengyel, M. & Gutkin, B. (2009). The role of ongoing dendritic oscillations in single-neuron dynamics. PLOS Comp. Bio. , 5(9)

Kret, M., Denollet, J., Grèzes, J. & De Gelder, B. (2011). The role of negative affectivity and social inhibition in perceiving social threat: an fMRI study. Neuropsychologia, 49(5), 1187-1193. doi:10.1016/j.neuropsychologia.2011.02.007

Gutkin, B., Tuckwell, H. & Jost, J. (2009). The Phenomenon of Inverse Stochastic Resonance. Naturwissenschaften, 96(9), 1091-7

Park, H. & Tallon-Baudry, C. (2014). The neural subjective frame: from bodily signals to perceptual consciousness. Philosophical Transactions Royal Society B, 369, 20130208. doi:10.1098/rstb.2013.0208

Tallon-Baudry, C., Florence, C., Hyeong-Dong, P. & Babo-Rebelo, M. (2018). The neural monitoring of visceral inputs, rather than attention, accounts for first-person perspective in conscious vision. Cortex, . doi:10.1016/j.cortex.2017.05.019

Dezecache, G., Grèzes, J. & Dahl, C. (2017). The nature and distribution of affiliative behaviour during exposure to mild threat. Royal Society open science, 4(8), 170265. doi:10.1098/rsos.170265

Mello, M. , Dupont, L. , Engelen, T., Acciarino, A. , W. de Borst, A. & de Gelder, B. (2022). The influence of body expression, group affiliation and threat proximity on interactions in virtual reality. Current Research in Behavioral Sciences, 3, 100075. doi:10.1016/j.crbeha.2022.100075

Patron, E., Mennella, R., Messerotti Benvenuti, S. & Thayer, J. (2019). The frontal cortex is a heart-brake: Reduction in delta oscillations is associated with heart rate deceleration. NeuroImage, 188, 403-410. doi:10.1016/j.neuroimage.2018.12.035

Patron, E., Mennella, R., Messerotti Benvenuti, S. & Thayer, J. (2018). The frontal cortex is a heart-brake: Reduction in delta oscillations is associated with heart rate deceleration. NeuroImage, 188, 403-410. doi:10.1016/j.neuroimage.2018.12.035

Beaurenaut, M., Tokarski , E., Dezecache, G. & Grèzes, J. (2020). The ‘Threat of Scream’ paradigm: a tool for studying sustained physiological and subjective anxiety. Scientific Reports, 10(1), 1-11. doi:10.1038/s41598-020-68889-0

Maex, R. & Gutkin, B. (2017). Temporal integration and 1/f power scaling in a circuit model of cerebellar interneurons. Journal of neurophysiology, 118(1), 471-485. doi:10.1152/jn.00789.2016

Guillou, L., Grandin, A. & Chevallier, C. (2021). Temporal discounting mediates the relationship between socio-economic status and social trust. Royal Society Open Science, 8(6). doi:10.1098/rsos.202104

Bouchacourt, F., Palminteri, S., Koechlin, E. & Ostojic, S. (2020). Temporal Chunking as a Mechanism for Unsupervised Learning of Task-Sets. eLife, 9, e50469. doi:10.7554/eLife.50469