Publications

Kouider, S., de Gardelle, V., Sackur, J. & Dupoux, E. (2010 ). How rich is consciousness? The partial awareness hypothesis. Trends in Cognitive Sciences, 14(7), 301-307. doi:10.1016/j.tics.2010.04.006

Kouider, S., de Gardelle, V., Dehaene, S., Dupoux, E. & Pallier, C. (2010). Cerebral bases of subliminal speech priming. Neuroimage, 49(1), 922-929. doi:10.1016/j.neuroimage.2009.08.043

Dupoux, E., de Gardelle, V. & Kouider, S. (2008). Subliminal speech perception and auditory streaming. Cognition, 109(2), 267-273. doi:10.1016/j.cognition.2008.06.012

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

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

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

Simeone, O. , Rajendran, B. , Gruning, A. , Eleftheriou, E. , Davies, M. , Denève, S. & Huang, G. (2019). Learning Algorithms and Signal Processing for Brain-Inspired Computing. IEEE Signal Processing Magazine, 36(6), 12-15. doi:10.1109/MSP.2019.2935557

Leptourgos, P., Bouttier, V., Jardri, R. & Denève, S. (2021). A functional theory of bistable perception based on dynamical circular inference. Plos Computational Biology, 16(12), e1008480. doi:10.1371/journal.pcbi.1008480

Zeldenrust, F., Gutkin, B. & Denève, S. (2021). Efficient and robust coding in heterogeneous recurrent networks. Plos Computational Biology, 17(4), e1008673. doi:10.1371/journal.pcbi.1008673

Recanatesi, S., Farrell, M., Lajoie, G., Denève, S., Rigotti, M. & Shea-Brown, E. (2021). Predictive learning as a network mechanism for extracting low-dimensional latent space representations. Nature Communications, 12(1417). doi:10.1038/s41467-021-21696-1

Schatz, T., Feldman, N., Goldwater, S., Cao, X. & Dupoux, E. (2021). Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input. In National Academy of Sciences (Eds.), Vol. 118: In Proceedings of the National Academy of Sciences, 7. doi:10.31234/osf.io/fc4wh

Rivière, M. & Dupoux, E. (2021). Towards unsupervised learning of speech features in the wild. In 2021 IEEE Spoken Language Technology Workshop (SLT), 156-163.

Er Jiang, B., Dunbar, E., Sonderegger, M., Clayards, M. & Dupoux, E. (2020). Modelling Perceptual Effects of Phonology with ASR Systems. In CogSci 2020-42nd Annual Virtual Meeting of the Cognitive Science Society.

Ludusan, B., Mazuka, R. & Dupoux, E. (2021). Does infant-directed speech help phonetic learning? A machine learning investigation. Cognitive Science, 45(5), e12946. doi:10.1111/cogs.12946

Rivière, M., Mazaré, P., Joulin, A. & Dupoux, E. (2020). Unsupervised pretraining transfers well across languages. In IEEE (Eds.), In ICASSP-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/ICASSP40776.2020.9054548