Publications

Rastelli, F., Tallon-Baudry, C., Migliaccio, R., Toba, M., Ducorps, A., Pradat-Diehl, P., Duret, C., Dubois, B., Valero-Cabré, A. & Bartolomeo, P. (2013). Neural dynamics of neglected targets in patients with right hemisphere damage. Cortex, 49(7), 1989-1996. doi:10.1016/j.cortex.2013.04.001

Hyafil, A., Giraud, A., Fontolan, L. & Gutkin, B. (2015). Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions. Trends in neurosciences, 38(11), 725-40. doi:10.1016/j.tins.2015.09.001

Kushnir, L. & Fusi, S. (2018). Neural Classifiers with Limited Connectivity and Recurrent Readouts. Journal of Neuroscience, 38 (46), 9900-9924. doi:10.1523/JNEUROSCI.3506-17.2018

Dogonasheva, O., Gutkin, B. & Zakharov, D. (2021). Multistability of coherent states in ring networks of type II neurons with asymmetrical nonlocal inhibitory connectivity. Journal of Computational Neuroscience. doi:10.1016/j.chaos.2021.111541

Dogonasheva, O., Kasatkin, D., Gutkin, B. & Zakharov, D. (2022). Multistability and evolution of chimera states in a network of type II Morris-Lecar neurons with asymmetrical nonlocal inhibitory connections. Chaos (Woodbury, N.Y.), 32(10), 101101. doi:10.1063/5.0117845

Sergent, C., Faugeras, F., Rohaut, B., Perrin, F., Valente, M., Tallon-Baudry, C., Cohen, L. & Naccache, L. (2017). Multidimensional cognitive evaluation of patients with disorders of consciousness using EEG: A proof of concept study. NeuroImage : Clinical, 13, 455-469. doi:10.1016/j.nicl.2016.12.004

Pacherie, E., Le Bars, S., Bourgeois-Gironde, S., Wyart, V., Sari, I. & Chambon, V. (2022). Motor coordination and strategic cooperation in joint action. Psychological science, 33(5), 736-751. doi:10.1177/09567976211053275

Caze, R., Humphries, M. & Gutkin, B. (2013). Modulation of computational capacity of neurons due to dendritic synaptic interactions. PLoS Comput. Biol., 9(2)

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

Graupner, M., Ahmed, S. & Gutkin, B. (2009). Modelling dynamics of addiction. Pharmacopsychiatry, 42(Suppl 1), S144-52

Graupner, M. & Gutkin, B. (2009). Modeling nicotinic neuromodulation from global functional and network levels to nAChR based mechanisms. Acta Pharmacologica Sinica, 30(6), 681-693. doi:10.1038/aps.2009.87

Krupa, M., Gutkin, B. & Zakharov, D. (2020). Modeling dopaminergic modulation of clustered gamma rhythms. Communications in Nonlinear Science and Numerical Simulation, 82(105086), 1-10. doi:10.1016/j.cnsns.2019.105086

Keramati, M., Ahmed, S. & Gutkin, B. (2017). Misdeed of the need: towards computational accounts of transition to addiction. Current opinion in neurobiology, 46, 142-153. doi:10.1016/j.conb.2017.08.014

Deperrois, N., Moiseeva, V. & Gutkin, B. (2019). Minimal Circuit Model of Reward Prediction Error Computations and Effects of Nicotinic Modulations. Frontiers in neural circuits, 12, 116. doi:10.3389/fncir.2018.00116

Oster, A., Faure, P. & Gutkin, B. (2015). Mechanisms for multiple activity modes of VTA dopamine neurons. Frontiers in computational neuroscience, 9, 95. doi:10.3389/fncom.2015.00095

Dipoppa, M., Krupa, M., Torcini, A. & Gutkin, B. (2012). Marginally Stable States and Quasi-periodic minor attractors in excitable pulse-coupled networks. SIADS , 63(1), 62-97

Dumont, G., Ermentrout, G. & Gutkin, B. (2017). Macroscopic phase-resetting curves for spiking neural networks. Physical review. E, 96, 042311. doi:10.1103/PhysRevE.96.042311

Wyart, V. (2018). Leveraging decision consistency to decompose suboptimality in terms of its ultimate predictability. Behavioral and Brain Sciences, 41, e248. doi:10.1017/S0140525X18001504

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

Lussange, J., Belianin, A., Bourgeois-Gironde, S. & Gutkin, B. (2021). Learning and Cognition in Financial Markets: A Paradigm Shift for Agent-Based Models. Advances in Intelligent Systems and Computing (Vol. 1252, pp. 241-255). doi:10.1007/978-3-030-55190-2_19

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

Babo-Rebelo, M., Wolpert, N., Adam, C., Hasboun, D. & Tallon-Baudry, C. (2016). Is the cardiac monitoring function related to the self in both the default network and right anterior insula? Philosophical Transactions B, 371(1708), 20160004. doi:10.1098/rstb.2016.0004

Buchin, A., Rieubland, S., Häusser, M., Gutkin, B. & Roth, A. (2016). Inverse Stochastic Resonance in Cerebellar Purkinje Cells. PLoS computational biology, 12(8), e1005000. doi:10.1371/journal.pcbi.1005000

Krupa, M., Gielen, S. & Gutkin, B. (2014). Intrinsic and synaptic mechanisms for clustered cortical gamma. J Comput. Neurosci , 37(2), 357-76

Engelen, T., Solca, M. & Tallon-Baudry, C. (2023). Interoceptive rhythms in the brain. Nature neuroscience, 26, 1670–1684. doi:10.1038/s41593-023-01425-1

Tallon-Baudry, C. (2023). Interoception: Probing internal state is inherent to perception and cognition. Neuron, . doi:10.1016/j.neuron.2023.04.019

Petzschner, F., Critchley, H. & Tallon-Baudry, C. (2022). Interoception. Scholarpedia, . doi:10.4249/scholarpedia.55569

Weiss, A., Chambon, V., Drugowitsch, J. & Wyart, V. (2021). Interacting with volatile environments stabilizes hidden-state inference and its brain signatures. Nature Communications, 12, 2228. doi:10.1038/s41467-021-22396-6

Tuckwell, H., Jost, J. & Gutkin, B. (2009). Inhibition and modulation of rhythmic neuronal spiking by noise. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 80(3). doi:10.1103/PhysRevE.80.031907

Balsdon, T., Summersby, S., Kemp, R. & White, D. (2018). Improving face identification with specialist teams. Cognitive Research: Principles and Implications, 3(1), 25. doi:10.1186/s41235-018-0114-7