Publications

Schuessler, F. , Dubreuil, A., Mastrogiuseppe, F., Ostojic, S. & Barak, O. (2020). Dynamics of random recurrent networks with correlated low-rank structure. Phys. Rev. Research, 2(1), 013111. doi:10.1103/PhysRevResearch.2.013111

Lourenço, J. , De Stasi, A., Deleuze, C. , Bigot, M. , Pazienti, A. , Aguirre, A. , Giugliano, M., Ostojic, S. & Bacci, A. (2020). Modulation of Coordinated Activity across Cortical Layers by Plasticity of Inhibitory Synapses. Cell reports, 30(3), 630-641.e5. doi:10.1016/j.celrep.2019.12.052

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

V Kuchibhotla, K. , Hindmarsh Sten, T., C. Papadoyannis, E. , Elnozahy, S. , Fogelson, K. , Chillale, R. , Boubenec, Y., C. Holland, P. , Ostojic, S. & C. Froemke, R. (2020). Author Correction: Dissociating task acquisition from expression during learning reveals latent knowledge. Nat Commun, 11(3176). doi:10.1038/s41467-020-17023-9

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

Martins, M. , Krause, C. , Neville, D. , Pino, D. , Villringer, A. & Obrig, H. (2019). Recursive hierarchical embedding in vision is impaired by posterior middle temporal gyrus lesions. Brain , 142(10), 3217-3229. doi:10.1093/brain/awz242