Publications

Dipoppa, M. & Gutkin, B. (2013). Correlations in background activity control persistent state stability and allow execution of working memory tasks. Frontiers in Computational Neuroscience, 7, 139. doi:10.3389/fncom.2013.00139

Wu, J., Gao, M., Shen, J., Shi, W., Oster, A. & Gutkin, B. (2013). Cortical control of VTA function and influence on nicotine reward. Biochemical Pharmacology, 86(8), 1173-1180. doi:10.1016/j.bcp.2013.07.013

Sidarus, N., Palminteri, S. & Chambon, V. (2019). Cost-benefit trade-offs in decision-making and learning. PLOS Computational Biology, 15(9), e1007326. doi:10.1371/journal.pcbi.1007326

Atkinson-Clement, C. , Maël, L., Patsalides, L. , De Liege, A. , Klein, Y. , Roze, E. , Deniau, E. , Andreas, H., Palminteri, S. & Yulia, W. (2022). Decision-making under risk and ambiguity in adults with Tourette syndrome. Psychological Medicine, 1-11. doi:10.1017/S0033291722002318

Fontanesi , L., Palminteri, S. & Lebreton, M. (2019). Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: A meta-analytical approach using diffusion decision modeling. Cognitive Affective Behavioral Neuroscience, 1-13. doi:10.3758/s13415-019-00723-1

Ting, C. , Palminteri, S., Engelmann, J. & Lebreton, M. (2019). Decreased confidence in loss-avoidance contexts is a primary meta-cognitive bias of human reinforcement learning. bioRxiv. doi:10.1101/593368

Caze, R., Humphries, M. & Gutkin, B. (2013). Dendrites enhance both single neuron and network computation. In Remme et al (eds) (Eds.), Dendritic ComputationSpringer

Safra, L., Chevallier, C. & Palminteri, S. (2019). Depressive symptoms are associated with blunted reward learning in social contexts. PLOS Computational Biology, 15(7), e1007224. doi:10.1371/journal.pcbi.1007224

Graupner, M. & Gutkin, B. (2012). Dynamical Approaches to understanding cholinergic control of nicotine action pathways in the dopaminergic reward circuits. Computational Neuroscience of Drug Addiction (Springer ed.).Ahmed and Gutkin (eds.)

Anlló, H., Katsumi, W., Sackur, J. & de Gardelle, V. (2022). Effects of false statements on visual perception hinge on social suggestibility. J Exp Psychol Hum Percept Perform, 48(8), 889–900. doi:10.1037/xhp0001024

Graupner, M., Maex, R. & Gutkin, B. (2013). Endogenous Cholinergic Inputs and Local Circuit Mechanisms Govern the Phasic Mesolimbic Dopamine Response to Nicotine. PLoS Computational Biology, 9(8). doi:10.1371/journal.pcbi.1003183

Delorme, C., Salvador, A., Valabrègue, R., Roze, E., Palminteri, S., Vidailhet, M., De Wit, S., Robbins, T., Hartmann, A. & Worbe, Y. (2016). Enhanced habit formation in Gilles de la Tourette syndrome. Brain : a journal of neurology, 139(Pt 2), 605-15. doi:10.1093/brain/awv307

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

Garcia, B., Lebreton, M., Bourgeois-Gironde, S. & Palminteri, S. (2023). Experiential values are underweighted in decisions involving symbolic options. Nature human behaviour, . doi:10.1038/s41562-022-01496-3

Dipoppa, M. & Gutkin, B. (2013). Flexible frequency control of cortical oscillations enables computations required for working memory. Proceedings of the National Academy of Sciences of the United States of America, 110(31), 12828-12833. doi:10.1073/pnas.1303270110

Keramati, M. & Gutkin, B. (2014). Homeostatic reinforcement learning for integrating reward collection and physiological stability. eLife, 3. doi:10.7554/eLife.04811

Sidarus, N., Haggard, P. & Beyer, F. (2018). How social contexts affect cognition: mentalizing interferes with sense of agency during voluntary action. PsyArXiv. doi:10.31234/osf.io/wj3ep

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

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

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

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

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

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

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

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

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)