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Publications

Book chapter  

de Vignemont, F. (2018). Was Descartes right after all? An affective background for bodily awareness. . In Tsakiris, Manos de Preester, Helena (Eds.), The Interoceptive Basis of the Mind: from homeostasis to awareness Oxford: Oxford University Press

Book chapter  

de Vignemont, F. (2018). Varieties of bodily feelings. In Kriegel, Uriah (Eds.), The Oxford handbook of consciousness Oxford: Oxford University Press

Book chapter  

de Vignemont, F. (2018). The first-person in pain. In Bain, D., Brady, M., Corns, J. (Eds.), Philosophy of Pain: Unpleasantness, Emotion, and DevianceRoutledge

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  

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

Book chapter  

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

Book chapter  

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

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  

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

Book chapter  

Ramus, F. (2013). A neurological model of dyslexia and other domain-specific developmental disorders with an associated sensorimotor syndrome. (pp. 75-102). doi:10.4324/9780203774915

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  

Ramus, F. & Szenkovits, G. (2011). Understanding the nature of the phonological deficit. (pp. 153-169). doi:10.4324/9780203838006

Book chapter  

Ramus, F., Peperkamp, S., Christophe, A., Jacquemot, C., Kouider, S. & Dupoux, E. (2010). A Psycholinguistic Perspective on the Acquisition of Phonology. In C. Fougeron (Eds.), Laboratory Phonology (Vol. 10 ).De Gruyter Mouton

Book chapter  

Darcy, I., Peperkamp, S. & Dupoux, E. (2007 ). Bilinguals play by the rules. Perceptual compensation for assimilation in late L2-learners. In Cole, J. and Hualde, J. (Eds.), Laboratory Phonology 9 (pp. 411-442). Berlin: Mouton de Gruyter

Book chapter  

Peperkamp, S. & Dupoux, E. (2007 ). Learning the mapping from surface to underlying representations in an artificial language. In Cole, J. and Hualde, J. (Eds.), Laboratory Phonology 9 (pp. 315-338). Berlin: Mouton de Gruyter

Book chapter  

Peperkamp, S. & Dupoux, E. (2002). A typological study of stress "deafness" In Gussenhoven, C. and Warner, N. (Eds.), Laboratory Phonology VII (pp. 203-240). Berlin: Mouton de Gruyter

Book chapter  

Peperkamp, S. & Dupoux, E. (2002). Coping with phonological variation in early lexical acquisition. In I. Lasser (Eds.), The Process of Language Acquisition (pp. 359-385). Frankfurt: Peter Lang

Other  

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

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  
Other  

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