Publications

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

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

de Vignemont, F. (2023). Affective bodily awareness.

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

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

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

de Vignemont, F. (2020). Bodily Feelings: Presence, Agency, and Ownership. In Uriah Kriegel (Eds.), The Oxford Handbook of the Philosophy of Consciousness Oxford: Oxford University Press. doi:10.1093/oxfordhb/9780198749677.013.4

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

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

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

de Vignemont, F. (2023). Désenchanter le corps.

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.)

de Vignemont, F. (2021). Feeling the world as being here. In F. de Vignemont, A. Serino, H.Y. Wong, A. Farnè (Eds.), The world at our fingertips: A multidisciplinary investigation of peripersonal space, Oxford : Oxford University Press

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

Imbert, M. (2020). La fin du regard éclairant. Une révolution dans les sciences de la vision au XIè siècle, Ibn al-Haytham.

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

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

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

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

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

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

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

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

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

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%)

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

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