Publications

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

Winkler, M. , Dumont, G., Schöll, E. & Gutkin, B. (2021). Phase response approaches to neural activity models with distributed delay. Bio Cybern. doi:10.1007/s00422-021-00910-9

Hyafil, A., Fontolan, L., Kabdebon, C., Gutkin, B. & Giraud, A. (2015). Speech encoding by coupled cortical theta and gamma oscillations. eLife, 4, 1-45. doi:10.7554/eLife.06213

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

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

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

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

Domenech, P., Rheims, S. & Koechlin, E. (2020 ). Neural Mechanisms resolving Exploitation-Exploration dilemmas in the prefrontal cortex. Science, 369(6507), eabb0184. doi:10.1126/science.abb0184

Koechlin, E. (2020). Executive Control and Decision-Making: a neural theory of prefrontal function. The Cognitive Neurosciences VI (pp. 451).

Koechlin, E. (2020). Human Decision-Making beyond the Rational Decision Theory. Trends in Cognitive Sciences, 24(1), 4-6. doi:10.1016/j.tics.2019.11.001

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

Chalk, M., Masset, P., Denève, S. & Gutkin, B. (2017). Sensory noise predicts divisive reshaping of receptive fields. PLoS computational biology, 13(6), e1005582. doi:10.1371/journal.pcbi.1005582

Chalk, M., Gutkin, B. & Denève, S. (2016). Neural oscillations as a signature of efficient coding in the presence of synaptic delays. eLife, . doi:10.7554/eLife.13824

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

Rooy, M., Lazarevich, I. , Koukouli, F., Maskos, U. & Gutkin, B. (2021). Cholinergic modulation of hierarchical inhibitory control over cortical resting state dynamics: Local circuit modeling of schizophrenia-related hypofrontality. Current Research in Neurobiology, 2, 1000018. doi:10.1016/j.crneur.2021.100018

Gutkin, B., Braun, P., Akila, M., Waltner, D. & Guhr, T. (2020). Exact local correlations in kicked chains. Phys. Rev. B, 102, 174307. doi:10.1103/PhysRevB.102.174307

Zakharov, D., Dogonasheva, O. & Gutkin, B. (2020). Role of Pyramidal Cell M-current in Weak Pyramidal/Interneuronal Gamma Cluster Formation. In 2020 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR), Innopolis, Russia, IEEE. doi:10.1109/DCNAIR50402.2020.9216942

Morozova, E., Faure, P., Gutkin, B., Lapish, C. & Kuznetsov, A. (2020). Distinct Temporal Structure of Nicotinic ACh Receptor Activation Determines Responses of VTA Neurons to Endogenous ACh and Nicotine. eNeuro, 7(4). doi:10.1523/ENEURO.0418-19.2020