Publications

Ecoffet, P., Fontbonne, N., Andre, J. & Bredeche, N. (2021). Reinforcement Learning with Rare Significant Events: Direct Policy Search vs. Gradient Policy Search. In GECCO '21: Genetic and Evolutionary Computation Conference, New YorkNYUnited States: Association for Computing Machinery. doi:10.1145/3449726

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.

Caze, R., Humphries, M. & Gutkin, B. (2012). Spiking and saturating dendrites differentially expand single neuron computation capacity. , Vol. 13: In Twenty First Annual Computational Neuroscience Meeting: CNS*2012, Decatur, GA, USA.

Mercier, H. & Sperber, D. (2021). L'Énigme de la raison.

Mercier, H. (2020 ). Not Born Yesterday. Princeton University Press

Gutkin, B. & Ahmed, S. (2012). Computational Neuroscience of Drug Addiction.

Dubourg, E. & Baumard, N. (2023). Do Fictions Impact People's Beliefs? A Critical View. The Routledge Handbook of Fiction and Belief (Routledge ed., pp. 18).

Chevallier, C. (2021). Vivre dans un environnement risqué : quels impacts pour la psychologie ? In Claudia Senik (Eds.), Sociétés en danger (Odile Jacob ed., pp. 197 à 208). Paris: La Découverte. doi:10.3917/dec.senik.2021.01.0197

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

Mercier, H. & Sperber, D. (2020). Bounded reason in a social world. In Ricardo Viale (Eds.), Routledge Handbook of Bounded Rationality

Chevallier, C. (2019). Theory of mind and autism: Revisiting Baron-Cohen et al.’s Sally-Anne study. In A. Slater and P. Quinn (Eds.), Developmental Psychology: Revisiting the Classic Studies 2nd edition (pp. 148-163).Sage

Mercier, H. (2019 in press). A paradox of information aggregation: We do it well but think about it poorly, and why this is a problem for institutions. In Ballantyne, N. & Dunning, D. (Eds.), Reason, Bias, and Inquiry: New Perspectives from the Crossroads of Epistemology and PsychologyOxford University Press

Baumard, N. & Cova, F. (2018). De la coopération à la culture. In Andler, Collins, Tallon-Baudry (Eds.), La cognition. Du neurone à la société (pp. 563-597). Paris: Gallimard

Dumont, G., Maex, R. & Gutkin, B. (2018). Dopaminergic Neurons in the Ventral Tegmental Area and Their Dysregulation in Nicotine Addiction. In Alan Anticevic and John D. Murray (Eds.), Computational Psychiatry: Mathematical Modeling of Mental Illness (pp. 47-84). doi:10.1016/B978-0-12-809825-7.00003-1

Gutkin, B. (2015). Theta-neurons. In Springer Verlag (Eds.), Encyclopedia of Comptutational Neuroscience (pp. 1034-1042).

Kuznetsov, A. & Gutkin, B. (2015). Dopaminergic cell Models. The Encyclopedia of Computational Neuroscience (pp. 2958-2965).

Remme, M., Lengyel, M. & Gutkin, B. (2015). Trade-off between dendritic democracy and independence in neurons with intrinsic subthreshold membrane potential oscillatio. In Remme et al (eds) (Eds.), Dendritic ComputationSpringer

Gutkin, B. & Stiefel, K. (2014). Cholinergic Neuromodulation of Phase Response Curves. In Schultheiss et al (eds) (Eds.), Phase Response Cruves in NeuroscienceSpringer

Remme, M., Lengyel, M. & Gutkin, B. (2014). Phase Response Methods in Dendritic Dynamics. In Schultheiss et al (eds) (Eds.), Phase Response Cruves in NeuroscienceSpringer

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.)