Publications

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

Rooy, M., Koukouli, F., Maskos, U. & Gutkin, B. (2018). Nicotinic modulation of hierarchal inhibitory control over prefrontal cortex resting state dynamics: modeling of genetic modification and schizophreniarelated pathology. bioRxiv, 301051. doi:10.1101/301051

Novikov, N. & Gutkin, B. (2018). Role of NMDA conductance in average firing rate shifts caused by external periodic forcing. arXiv , 1812.03609

Neri, P. (2018). Classification images as descriptive statistics. "Journal of Mathematical Psychology", "82"("1), "26–37"

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

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

Dumont, G. & Gutkin, B. (2018). Macroscopic phase resetting-curves determine oscillatory coherence and signal transfer in inter-coupled neural circuits. arXiv , 1812.03455

Deperrois, N. & Gutkin, B. (2018). Nicotinic and Cholinergic Modulation of Reward Prediction Error Computations in the Ventral Tegmental Area: a Minimal Circuit Model. bioRxiv, 423806. doi:10.1101/423806

Mamassian, P. & Coen-Cagli, R. (2018). An Ideal Observer Model to Probe Human Visual Segmentation of Natural Images. arXiv

Vacher, J. , Davila, A., Kohn, A. & Coen-Cagli, R. (2020). Texture Interpolation for Probing Visual Perception. Advances in Neural Information Processing Systems (Spotlight – top 5%)

Le Coënt, A., Fribourg, L., Vacher, J. & Wisniewski, R. (2020). Probabilistic reachability and control synthesis for stochastic switched systems using the tamed Euler method. Nonlinear Analysis: Hybrid Systems, 36, 100860Elsevier. doi:10.1016/j.nahs.2020.100860

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

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

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

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.)

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

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

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

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

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

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

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

de Vignemont, F. & Sackur, J. (2018). La conscience. In Collins, Thérèse, Tallon-Baudry, Catherine, Andler, Daniel (Eds.), La cognitionGallimard

de Vignemont, F. (2018). Schéma corporel et image corporelle. In De Boeck/Solal (Eds.), Manuel de psychomotricité

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

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

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

de Vignemont, F. (2019). Against phenomenal parsimony: a plea for bodily feelings. In Goldman, Alvin McLaughlin, McLaughlin (Eds.), Metaphysics and cognitive science Oxford: Oxford University Press

de Vignemont, F. (2019). The phenomenology of bodily ownership. In Guillot, Marie Garcia-Carpintero, Manuel (Eds.), The sense of mineness Oxford: Oxford University Press

Alsmith, A. & Longo, M. (2019). Using VR technologies to investigate the flexibility of human self-conception. In E. Fischer and M. Curtis (Eds.), Methodological Advances in Experimental Philosophy (pp. 153-174). London: Bloomsbury