Publications

Chiril, P. , Moriceau, V. , Benamara, F. , Mari, A., Origgi, G. & Coulomb-Gully, M. (2020). An Annotated Corpus for Sexism Detection in French Tweets. In LREC 2020, Marseille, EuropeanLanguageResourcesAssociation (ELRA), 1397–1403.

Bouvier, A. (2019). Individualism versus Holism. SAGE Research Methods Foundations, . doi:10.4135/9781526421036803102

Bouvier, A. (2020). La théorie des croyances collectives de Pareto. Essai de reconstruction et d’évaluation de la théorie des ‘dérivations’ et des ‘résidus’ du point de vue des recherches contemporaines. The Lab’s Quaterly, a.XXII(3), 85-108

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

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

Bernard, M., Thiollière, R., Saksida, A., Loukatou, G., Larsen, E., Johnson, M., Fibla, L., Dupoux, E., Daland, R., Cao, X. & Cristia, A. (2020). WordSeg: Standardizing unsupervised word form segmentation from text. Behavior research methods, 52(1), 264-278. doi:10.3758/s13428-019-01223-3

Chaabouni, R., Kharitonov, E., Dupoux, E. & Baroni, M. (2021). Communicating artificial neural networks develop efficient color-naming systems. PNAS, 118(12), e2016569118. doi:10.1073/pnas.2016569118

Zeghidour, N., Usunier, N., Synnaeve, G., Collobert, R. & Dupoux, E. (2019). End-to-End Speech Recognition from the raw waveform. In Interspeech-2018.

Martins, M. , Bianco, R. , Sammler, D. & Villringer, A. (2019). Recursion in action: An fMRI study on the generation of new hierarchical levels in motor sequences. Human Brain Mapping , 40(4). doi:10.1002/hbm.24549

Udden, J. , Martins, M. , Zuidema, W. & Tecumseh Fitch, W. (2019). Hierarchical Structure in Sequence Processing: How to Measure It and Determine Its Neural Implementation. Topics in Cognitive Science . doi:10.1111/tops.12442