Publications

Correa, C. , Noorman, S. , Jiang, J. , Palminteri, S., Cohen, M. , Lebreton, M. & van Gaal, S. (2018). How the level of reward awareness changes the computational and electrophysiological signatures of reinforcement learning. Journal of Neuroscience , 0457-18. doi:10.1523/JNEUROSCI.0457-18.2018

Balsdon, T. & Clifford, C. (2018). How wide is the cone of direct gaze? Royal Society open science 6, 5(8), 180249

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

Jacquet, P., Wyart, V., Desantis, A., Hsu , Y. , Granjon, L. , Sergent, C. & Waszak, F. (2018). Human susceptibility to social influence and its neural correlates are related to perceived vulnerability to extrinsic morbidity risks. Scientific Reports, 8, 13347. doi:10.1038/s41598-018-31619-8

Zhang, D., Gao, M., Xu, D., Shi, W., Gutkin, B., Steffensen, S., Lukas, R. & Wu, J. (2012). Impact of prefrontal cortex in nicotine-induced excitation of ventral tegmental area dopamine neurons in anesthetized rats. Journal of Neuroscience, 32(36), 12366-12375. doi:10.1523/JNEUROSCI.5411-11.2012

Canavier, C., Evans, R., Oster, A., Pissadaki, E., Drion, G., Kuznetsov, A. & Gutkin, B. (2016). Implications of cellular models of dopamine neurons for disease. Journal of neurophysiology, 116(6), 2815-2830. doi:10.1152/jn.00530.2016

Findling, C., Chopin, N. & Koechlin, E. (2021). Imprecise neural computations as a source of adaptive behaviour in volatile environments. Nature Human Behaviour , 5, 99-112. doi:10.1038/s41562-020-00971-z

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

Chambon, V., Thero, H., Vidal, M., Vandendriessche, H., Haggard, P. & Palminteri, S. (2020). Information about action outcomes differentially affects learning from self-determined versus imposed choices. Nature Human Behaviour , 4, 1067-1079. doi:10.1038/s41562-020-0919-5

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

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

Najar, A., Sigaud, O. & Chetouani , M. (2020). Interactively shaping robot behaviour with unlabeled human instructions. Auton Agent Multi-Agent Syst, 34(35). doi:10.1007/s10458-020-09459-6

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

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

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

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

Salem-Garcia, N., Palminteri, S. & Lebreton, M. (2023). Linking confidence biases to reinforcement-learning processes. Psychological review, . doi:10.1037/rev0000424

Dumont, G. & Gutkin, B. (2018). Macroscopic phase resetting-curves determine oscillatory coherence and signal transfer in inter-coupled neural circuits. arXiv , 1812.03455

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

Mansouri, F., Koechlin, E., Rosa, M. & Buckley, M. (2017). Managing competing goals - a key role for the frontopolar cortex. Nature reviews. Neuroscience, 18(11), 645-657. doi:10.1038/nrn.2017.111

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

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

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

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

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

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

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

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

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

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