Publications

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

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

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.)

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. (2014). Cholinergic Neuromodulation of Phase Response Curves. 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

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

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

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

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.

Mamassian, P., Kersten, D. & Knill, D. (1996). Categorical local-shape perception. Perception, 25(1), 95-107. doi:10.1068/p250095

Mamassian, P. & Kersten, D. (1996). Illumination, shading and the perception of local orientation. Vision Research, 36(15), 2351-2367. doi:10.1016/0042-6989(95)00286-3

Kersten, D., Knill, D., Mamassian, P. & Bülthoff, I. (1996). Illusory motion from shadows. Nature, 379(6560), 31. doi:10.1038/379031a0

Mamassian, P. (1997). Prehension of objects oriented in three-dimensional space. Experimental Brain Research, 114(2), 235-245. doi:10.1007/PL00005632

Knill, D., Mamassian, P. & Kersten, D. (1997). Geometry of shadows. Journal of the Optical Society of America a-Optics Image Science and Vision, 14(12), 3216-3232. doi:10.1364/JOSAA.14.003216

Kersten, D., Mamassian, P. & Knill, D. (1997). Moving cast shadows induce apparent motion in depth. Perception, 26(2), 171-192. doi:10.1068/p260171

Mamassian, P. & Landy, M. (1998). Observer biases in the 3D interpretation of line drawings. Vision Research, 38(18), 2817-2832. doi:10.1016/S0042-6989(97)00438-0

Mamassian, P., Knill, D. & Kersten, D. (1998). The perception of cast shadows. Trends in Cognitive Sciences, 2(8), 288-295. doi:10.1016/S1364-6613(98)01204-2

Adams, W. & Mamassian, P. (2002). Common mechanisms for 2D tilt and 3D slant after-effects. Vision Research, 42(23), 2563-2568. doi:10.1016/S0042-6989(02)00269-9

Barthelmé, S. & Mamassian, P. (2009). Evaluation of objective uncertainty in the visual system. PLoS Computational Biology, 5(9), e1000504. doi:10.1371/journal.pcbi.1000504

Lunghi, C., Binda, P. & Morrone, M. (2010). Touch disambiguates rivalrous perception at early stages of visual analysis. Current Biology, 20(4). doi:10.1016/j.cub.2009.12.015

Oster, A. & Gutkin, B. (2011). A reduced model of DA neuronal dynamics that displays quiescence, tonic firing and bursting. Journal of Physiology Paris, 105, 53-58. doi:10.1016/j.jphysparis.2011.07.012

Muller, L., Brette, R. & Gutkin, B. (2011). Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike phase-locking. Frontiers in Computational Neuroscience, 15(5), 45. doi:10.3389/fncom.2011.00045

Lunghi, C., Burr, D. & Morrone, C. (2011). Brief periods of monocular deprivation disrupt ocular balance in human adult visual cortex. Current Biology, 21(14). doi:10.1016/j.cub.2011.06.004

Keramati, M. & Gutkin, B. (2011). A Reinforcement Learning Theory for Homeostatic Regulation. NIPS, .

Jedlicka, P., Deller, T., Gutkin, B. & Backus, K. (2011). Activity-dependent intracellular chloride accumulation and diffusion controls GABA(A) receptor-mediated synaptic transmission. Hippocampus, 21(8), 885-98. doi:10.1002/hipo.20804

Zhang, D., Gao, M., Xu, D., Shi, W., Gutkin, B., Steffensen, S., Lukas, R. & Wu, J. (2012). Impact of prefrontal cortex in nicotine-induced excitation of ventral tegmental area dopamine neurons in anesthetized rats. Journal of Neuroscience, 32(36), 12366-12375. doi:10.1523/JNEUROSCI.5411-11.2012