Publications

Hernán, A., Joshua, H. & Sackur, J. (2021). Deployment dynamics of hypnotic anger modulation. Consciousness and Cognition, 91, 103118. doi:10.1016/j.concog.2021.103118

Palminteri, S. & Maël, L. (2022). The computational roots of positivity and confirmation biases in reinforcement-learning. Trends in Cognitive Sciences, 26(7), 607-621. doi:10.1016/j.tics.2022.04.005

Cleeremans, A. & Tallon-Baudry, C. (2022). Consciousness matters: phenomenal experience has functional value. Neuroscience of Consciousness., 2022(1), niac007. doi:10.1093/nc/niac007

Delorme, C., Salvador, A., Valabrègue, R., Roze, E., Palminteri, S., Vidailhet, M., De Wit, S., Robbins, T., Hartmann, A. & Worbe, Y. (2016). Enhanced habit formation in Gilles de la Tourette syndrome. Brain : a journal of neurology, 139(Pt 2), 605-15. doi:10.1093/brain/awv307

Tallon-Baudry, C. (2021). From global to local in conscious vison: behavior & MEG. Journal of vision, 21, 63. doi:10.1167/jov.21.9.63

Chambon, V., Thero, H., Vidal, M., Vandendriessche, H., Haggard, P. & Palminteri, S. (2020). Information about action outcomes differentially affects learning from self-determined versus imposed choices. Nature Human Behaviour , 4, 1067-1079. doi:10.1038/s41562-020-0919-5

Lussange, J., Lazarevich, I. , Bourgeois-Gironde, S., Palminteri, S. & Gutkin, B. (2020). Modelling Stock Markets by Multi-agent Reinforcement Learning. Computational Economics, 57, 113-147. doi:10.1007/s10614-020-10038-w

Bernard, A., Bredeche, N. & Andre, J. (2020). Indirect genetic effects allow escape from the inefficient equilibrium in a coordination game. Evolution Letters, 4(3), 257-265. doi:10.1002/evl3.155

Buot, A., Azzalini, D., Chaumon, M. & Tallon-Baudry, C. (2021). Does stroke volume influence heartbeat evoked responses? Biological Psychology. doi:10.1016/j.biopsycho.2021.108165

Candia-Rivera, D. , Annen, J., Gosseries, O. , Martial, C. , Thibaut, A. , Laureys, S. & Tallon-Baudry, C. (2021). Neural responses to heartbeats detect residual signs of consciousness during resting state in post-comatose patients. Journal of Neuroscience, 41(24). doi:10.1523/JNEUROSCI.1740-20.2021

Rebollo, I., Wolpert, N. & Tallon-Baudry, C. (2021). Brain–stomach coupling: Anatomy, functions, and future avenues of research. Current Opinion in Biomedical Engineering, 18, 100270. doi:10.1016/j.cobme.2021.100270

Wolpert, N., Rebollo, I. & Tallon-Baudry, C. (2020). Electrogastrography for psychophysiological research: Practical considerations, analysis pipeline, and normative data in a large sample. Psychophysiology, 57(9), e 13599. doi:10.1111/psyp.13599

Pischedda, D. , Palminteri, S. & Giorgio, C. (2020). The effect of counterfactual information on outcome value coding in medial prefrontal and cingulate cortex: from an absolute to a relative neural code. Journal of Neuroscience, 40(16), 3268-3277. doi:10.1523/JNEUROSCI.1712-19.2020

Palminteri, S. & Maël, L. (2021). Context-dependent outcome encoding in human reinforcement learning. Current Opinion in Behavioral Sciences, 41, 144-151. doi:10.1016/j.cobeha.2021.06.006

Bavard, S., Rustichini, A. & Palminteri, S. (2021). Two sides of the same coin: Beneficial and detrimental consequences of range adaptation in human reinforcement learning. Science Advances, 7(14). doi:10.1126/sciadv.abe0340

Skvortsova , V., Palminteri, S., Buot, A., Karachi, C., Welter, M., Grabli, D. & Pessiglione, M. (2020). A Causal Role for the Pedunculopontine Nucleus in Human Instrumental Learning. Current Biology, 31(5). doi:10.1016/j.cub.2020.11.042

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

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

Zeldenrust, F., De Knecht, S., Wadman, W., Denève, S. & Gutkin, B. (2017). Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series. Frontiers in computational neuroscience, 11, 49. doi:10.3389/fncom.2017.00049

Jardri, R. & Denève, S. (2017). [Jumping-to-conclusions in schizophrenia is mediated by circular inference]. Medecine sciences : M/S, 33(11), 933-935. doi:10.1051/medsci/20173311006

Leptourgos, P., Denève, S. & Jardri, R. (2017). Can circular inference relate the neuropathological and behavioral aspects of schizophrenia? Current opinion in neurobiology, 46, 154-161. doi:10.1016/j.conb.2017.08.012

Denève, S., Alemi, A. & Bourdoukan, R. (2017). The Brain as an Efficient and Robust Adaptive Learner. Neuron, 94(5), 969-977. doi:10.1016/j.neuron.2017.05.016

Jardri, R., Duverne, S., Litvinova, A. & Denève, S. (2017). Experimental evidence for circular inference in schizophrenia. Nature communications, 8, 14218. doi:10.1038/ncomms14218

Koren, V. & Denève, S. (2017). Computational Account of Spontaneous Activity as a Signature of Predictive Coding. PLoS computational biology, 13(1), e1005355. doi:10.1371/journal.pcbi.1005355

Gutierrez, G. & Denève, S. (2018). Population adaptation in efficient balanced networks. eLife, 8, e46926. doi:10.7554/eLife.46926

Recanatesi, S., Farrell, M., Lajoie, G., Denève, S., Rigotti, M. & Shea-Brown, E. (2018). Signatures and mechanisms of low-dimensional neural predictive manifolds. bioRxiv. doi:10.1101/471987

Alemi, A., Machens, C., Denève, S. & Slotine, J. (2018). Learning nonlinear dynamics in efficient, balanced spiking networks using local plasticity rules. In Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, AAAI Press.

Leptourgos, P., Notredame, C., Jardri, R., Denève, S. & Eck, M. (2020). Circular inference in bistable perception. Journal of Vision, 20(12), 521195. doi:10.1167/jov.20.4.12

Jardri, R., Leptourgos, P., Bouttier, V. & Denève, S. (2020). Circular Inference and Hallucinations. Biological Psychiatry, 87(9), S41-S42. doi:10.1016/j.biopsych.2020.02.131

Brendel, W. , Bourdoukan, R., Vertechi, P. , Machens, C. & Denève, S. (2020). Learning to represent signals spike by spike. PLOS Computational Biology, 16(3), e1007692. doi:10.1371/journal.pcbi.1007692