Publications

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

Zakharov, D., Dogonasheva, O. & Gutkin, B. (2021). Bistability of globally synchronous and chimera states in a ring of phase oscillators coupled by a cosine kernel. In 2021 5th Scientific School Dynamics of Complex Networks and their Applications (DCNA), 211-214. doi:10.1109/DCNA53427.2021.9586968

Dogonasheva, O., Gutkin, B. & Zakharov, D. (2021). Calculation of travelling chimera speeds for dynamical systems with ring topologies. In 5th Scientific School Dynamics of Complex Networks and their Applications (DCNA), 61-64. doi:10.1109/DCNA53427.2021.9586903

Radushev, D. , Dogonasheva, O., Gutkin, B. & Zakharov, D. (2023). Chimera states in a ring of non-locally connected interneurons. In 7th Scientific School Dynamics of Complex Networks and their Applications (DCNA), Kaliningrad, Russian Federation, 229-232. doi:10.1109/DCNA59899.2023.10290318

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

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

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

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

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

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.)

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).

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

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.

Grèzes, J. (2011). Les émotions, modes d’action et de communication. Med Sci (Paris), 27(8-9), 683-684. doi:10.1051/medsci/2011278001

Grèzes, J., Erblang, M. , Vilarem, E., Quiquempoix, M. , Van Beers, P. , Guillard, M. , Sauvet, F. , Mennella, R. & Rabat, A. (2022). Modifications des décisions d’évitement de visages menaçants lors d’une privation totale de sommeil en lien avec l’humeur positive des individus. Médecine du Sommeil, 19(1), 29. doi:10.1016/j.msom.2022.01.119

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

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

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

Zakharov, D., Dogonasheva, O. & Gutkin, B. (2020). Role of Pyramidal Cell M-current in Weak Pyramidal/Interneuronal Gamma Cluster Formation. In 2020 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR), Innopolis, Russia, IEEE. doi:10.1109/DCNAIR50402.2020.9216942

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.

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

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

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

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