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

Mamassian, P., Landy, M., Maloney, L., Rao, R., Olshausen, B. & Lewicki, M. (2002). Bayesian modelling of visual perception. In R. Rao, B. Olshausen & M. Lewicki (Eds.), Probabilistic Models of the Brain: Perception and Neural Function (pp. 13-36). Cambridge, MA: MIT Press

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

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

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.)

Chemla, E. & Spector, B. (2010 ). Experimental Detection of Embedded Implicatures. In Aloni, Maria and Bastiaanse, Harald and de Jager, Tikitu and Schulz, Katrin (Eds.), In Logic, Language and Meaning: 17th Amsterdam Colloquium, Amsterdam, The Netherlands, Springer Berlin Heidelberg, 53–62. doi:10.1007/978-3-642-14287-1_6

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

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

Erdmann, A. , Joseph Wrisley, D., Allen, B. , Brown, C. , Cohen-Bodenes, S., Elsner, M. , Feng, Y. , D Joseph, B. , Joyeux-Prunel, B. & de Marneffe, M. (2019). Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities. In Proceedings of the 2019 Conference of the North, 2223-2234. doi:10.18653/v1/N19-1231

Cremers, A. & Chemla, E. (2017). Probability Judgments of Gappy Sentences. In Pistoia-Reda, Salvatore and Filippo Domaneschi (Eds.), Linguistic and Psycholinguistic Approaches on Implicatures and Presuppositions (pp. 111-150).Palgrave

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

Zuberbühler, K., Chemla, E. & Schlenker, P. (2021). Stereotyped vocalizations. Encyclopedia of Evolutionary Psychological Science (pp. 7970-7974).Springer. doi:10.1007/978-3-319-16999-6_3330-1

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

Millet, J., Caucheteux, C. , Boubenec, Y., Gramfort, A., Dunbar, E., Pallier, C. & King, J. (2022). Toward a realistic model of speech processing in the brain with self-supervised learning. , Vol. 35: In 36th Conference on Neural Information Processing Systems, 33428-33443.

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