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Publications

Other  

Lussange, J., Belianin, A., Bourgeois-Gironde, S. & Gutkin, B. (2017). A bright future for financial agent-based models. arXiv preprint arXiv:1801.08222

Non-reviewed conference proceeding  

Caze, R., Humphries, M., Gutkin, B. & Schultz, S. (2013). A difficult classification for neurons without dendrites. In Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on, San Diego, CA, USA, IEEE, 215-218. doi:10.1109/NER.2013.6695910

Other  

Martinez-Saito, M. , Konovalov, R. , Piradov, M. , Shestakova, A. , Gutkin, B. & Klucharev, V. (2018). Action in auctions: neural and computational mechanisms of bidding behavior. BioRxiv, 464925. doi:10.1101/464925

Other  

Tallon-Baudry, C. (2013). Catherine Tallon-Baudry. Current Biology, 23(14), R588-R590. doi:10.1016/j.cub.2013.06.010

Book chapter  

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

Book chapter  

Grèzes, J. & Dezecache, G. (2012). Communication émotionnelle: mécanismes cognitifs et cérébraux. In P. Allain, G. Aubin & D. Le Gal (Eds.), Cognition Sociale et NeuropsychologieSolal

Book chapter  

Batty, M., Kovarski, K. & Meaux, E. (2018). De l’exploration à la perception des visages chez les personnes avec un trouble du spectre de l’autisme. Neuropsychologie et remédiations des troubles du spectre de l’autismeEditions deboeck Supérieur

Other  

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

Book chapter  

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

Other  

Romagnoni, A. , Colonnese, M. , Touboul, J. & Gutkin, B. (2018). Development of inhibitory synaptic delay drives maturation of thalamocortical network dynamics. bioRxiv, 296673. doi:10.1101/296673

Non-reviewed conference proceeding  

Dubreuil, A., Valente, A., Mastrogiuseppe, F. & Ostojic, S. (2019). Disentangling the roles of dimensionality and cell classes in neural computation. In NeurIPS Workshop.

Book chapter  

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

Book chapter  

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

Book chapter  

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.)

Book chapter  

Dezecache, G., Eskenazi, T. & Grèzes, J. (2016). Emotional Convergence: A Case of Contagion? In Sukhvinder D. Obhi & Emily S. Cross (Eds.), Shared Representations: Sensorimotor Foundations of Social Life (Cambridge University Press ed., pp. 417).

Book chapter  

Koechlin, E. (2020). Executive Control and Decision-Making: a neural theory of prefrontal function. The Cognitive Neurosciences VI (pp. 451-68).

Other  

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

Reviewed conference proceeding  

Lussange, J., Belianin, A., Bourgeois-Gironde, S. & Gutkin, B. (2020). Learning and Cognition in Financial Markets: A Paradigm Shift for Agent-Based Models. In Arai K., Kapoor S., Bhatia R. (Eds.), Vol. 1252: In IntelliSys 2020. Advances in Intelligent Systems and Computing, 241-255. doi:10.1007/978-3-030-55190-2_19

Reviewed conference proceeding  

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.

Other  

Lazarevich, I. , Gutkin, B. & Prokin, I. (2018). Neural activity classification with machine learning models trained on interspike interval series data. arxiv , 1810.03855

Reviewed conference proceeding  

Hicheur, H., Kadone, H., Grèzes, J. & Berthoz, A. (2013). Perception of emotional gaits using avatar animation of real and artificially synthesized gaits. In 5th Biannual Conference of the Humaine-Association on Affective Computing and Intelligent Interaction (ACII), Geneva, Switzerland, IEEE, 460-466. doi:10.1109/ACII.2013.82

Book chapter  

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

Book chapter  

Gutkin, B. & Stiefel, K. (2007). Phase-resetting curves and neuromodulation of action potential dynamics in the cortex. (Vol. 40, pp. 14-15).

Other  
Other  

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

Reviewed conference proceeding  

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.

Book chapter  

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