Publications

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

Zeldenrust, F., De Knecht, S., Wadman, W., Denève, S. & Gutkin, B. (2017). Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series. Frontiers in computational neuroscience, 11, 49. doi:10.3389/fncom.2017.00049

Zeldenrust, F., Gutkin, B. & Denève, S. (2021). Efficient and robust coding in heterogeneous recurrent networks. Plos Computational Biology, 17(4), e1008673. doi:10.1371/journal.pcbi.1008673

Zamani, A. , Novikov, N. & Gutkin, B. (2020). Concomitance of inverse stochastic resonance and stochastic resonance in a minimal bistable spiking neural circuit. Communications in Nonlinear Science and Numerical Simulation, 82, 105024. doi:10.1016/j.cnsns.2019.105024

Zakharov, D., Krupa, M., Gutkin, B. & Kuznetsov, A. (2018). High-frequency forced oscillations in neuronlike elements. Physical Review E , 97(6). doi:10.1103/PhysRevE.97.062211

Zakharov, D., Lapish, C., Gutkin, B. & Kuznetsov, A. (2016). Synergy of AMPA and NMDA Receptor Currents in Dopaminergic Neurons: A Modeling Study. Frontiers in computational neuroscience, 10, 48. doi:10.3389/fncom.2016.00048

Wu, J., Gao, M., Shen, J., Shi, W., Oster, A. & Gutkin, B. (2013). Cortical control of VTA function and influence on nicotine reward. Biochemical Pharmacology, 86(8), 1173-1180. doi:10.1016/j.bcp.2013.07.013

Wexler, M., Duyck, M. & Mamassian, P. (2015). Persistent states in vision break universality and time invariance. Proceedings of the National Academy of Sciences of the United States of America, 112(48), 14990-14995. doi:10.1073/pnas.1508847112

Volk, D., Dubinin, I., Gutkin, B., Myasnikova, A. & Nikulin, V. (2018). Cross-Frequency Synchrony Analysis. Frontiers in Neuroinformatics , 12, 72. doi:10.3389/fninf.2018.00072

Van Der Groen, O., Van Der Burg, E., Lunghi, C. & Alais, D. (2013). Touch Influences Visual Perception with a Tight Orientation-Tuning. Plos One, 8(11). doi:10.1371/journal.pone.0079558

Vacher, J. , Mamassian, P. & Coen-Cagli, R. (2020). Measuring and Modeling Human Probabilistic Segmentation Maps. Journal of Vision, 20(11), 260. doi:10.1167/jov.20.11.260

Vacher, J. , Mamassian, P. & Coen-Cagli, R. (2019). An Ideal Observer Model for Grouping and Contour Integration in Natural Images. Vision, 48, 88-88

Tran-Van-Minh, A., Caze, R., Abrahamsson, T., Cathala, L., Gutkin, B. & Digregorio, D. (2015). Contribution of sublinear and supralinear dendritic integration to neuronal computations. Frontiers in cellular neuroscience, 9, 67. doi:10.3389/fncel.2015.00067

Toscani, M. , Mamassian, P. & Valsecchi, M. (2021). Underconfidence in peripheral vision. Journal of Vision, 21(2). doi:10.1167/jov.21.6.2

Toscani, M. , Gegenfurtner, K. , Mamassian, P. & Valsecchi, M. (2019). Under-confidence in peripheral vision. Journal of Vision, 19(10), 67c. doi:10.1167/19.10.67c

Tolu, S., Eddine, R., Marti, F., David, V., Graupner, M., Pons, S., Baudonnat, M., Husson, M., Besson, M., Reperant, C., Zemdegs, J., Pagès, C., Hay, Y., Lambolez, B., Caboche, J., Gutkin, B., Gardier, A., Changeux, J., Faure, P. & Maskos, U. (2013). Co-activation of VTA da and GABA neurons mediates nicotine reinforcement. Molecular Psychiatry, 18(3), 382-393. doi:10.1038/mp.2012.83

Simeone, O. , Rajendran, B. , Gruning, A. , Eleftheriou, E. , Davies, M. , Denève, S. & Huang, G. (2019). Learning Algorithms and Signal Processing for Brain-Inspired Computing. IEEE Signal Processing Magazine, 36(6), 12-15. doi:10.1109/MSP.2019.2935557

Schweitzer, R. , Watson, T. , Balsdon, T. & Rolfs, M. (2018). From retinal to world-centered perception of intra-saccadic motion streaks: Evidence for high-fidelity eye position information during saccades. , 18(10), 1285

Schütz, A. & Mamassian, P. (2016). Early, local motion signals generate directional preferences in depth ordering of transparent motion. Journal of vision, 16(10), 24. doi:10.1167/16.10.24

Schaffhauser, T., Boubenec, Y. & Mamassian, P. (2019). Temporal binding across senses facilitates change detection within senses. Journal of vision, 19(10), 19a-19a. doi:10.1167/19.10.19a

Rooy, M., Lazarevich, I. , Koukouli, F., Maskos, U. & Gutkin, B. (2021). Cholinergic modulation of hierarchical inhibitory control over cortical resting state dynamics: Local circuit modeling of schizophrenia-related hypofrontality. Current Research in Neurobiology, 2, 1000018. doi:10.1016/j.crneur.2021.100018

Recht, S. , Mamassian, P. & de Gardelle, V. (2018). Confidence blinks before attention. Journal of Vision, 18(10), 1112. doi:10.1167/18.10.1112

Recht, S. , de Gardelle, V. & Mamassian, P. (2021). Metacognitive blindness in temporal selection during the deployment of spatial attention. Cognition, 216, 104864. doi:10.1016/j.cognition.2021.104864

Recht, S. , de Gardelle, V. & Mamassian, P. (2019). Metacognitive estimates of time during spatial orienting of attention. Journal of vision, 19(10), 214c-214c. doi:10.1167/19.10.214c

Recht, S. , Mamassian, P. & de Gardelle, V. (2019). Temporal attention causes systematic biases in visual confidence. Scientific Reports, 9(1), 11622. doi:10.1038/s41598-019-48063-x

Recht, S. , Mamassian, P. & de Gardelle, V. (2018). When temporal attention lacks confidence. bioRxiv, 496232

Recanatesi, S., Farrell, M., Lajoie, G., Denève, S., Rigotti, M. & Shea-Brown, E. (2021). Predictive learning as a network mechanism for extracting low-dimensional latent space representations. Nature Communications, 12(1417). doi:10.1038/s41467-021-21696-1

Rahnev, D., Desender, K., Lee, A., Adler, W., Aguilar-Lleyda, D., Akdoğan, B., Arbuzova, P., Atlas, L., Balcı, F., Bang, J., Bègue, I., Birney, D., Brady, T., Calder-Travis, J., Chetverikov, A., Clark, T., Davranche, K., Denison, R., Dildine, T., Double, K., Duyan, Y., Faivre, N., Fallow, K., Filevich, E., Gajdos, T., Gallagher, R., de Gardelle, V., Gherman, S., Haddara, N., Hainguerlot, M., Hsu, T., Hu, X., Iturrate, I., Jaquiery, M., Kantner, J., Koculak, M., Konishi, M., Koß, C., Kvam, P., Kwok, S., Lebreton, M., Lempert, K., Ming Lo, C., Luo, L., Maniscalco, B., Martin, A., Massoni, S., Matthews, J., Mazancieux, A., Merfeld, D., O'Hora, D., Palser, E., Paulewicz, B., Pereira, M., Peters, C., Philiastides, M., Pfuhl, G., Prieto, F., Rausch, M., Recht, S. , Reyes, G., Rouault, M., Sackur, J., Sadeghi, S., Samaha, J., Seow, T., Shekhar, M., Sherman, M., Siedlecka, M., Skóra, Z., Song, C., Soto, D., Sun, S., Van Boxtel, J., Wang, S., Weidemann, C., Weindel, G., Wierzchoń, M., Xu, X., Ye, Q., Yeon, J., Zou, F. & Zylberberg, A. (2020). The Confidence Database. Nature Human Behaviour, 4, 317–325. doi:10.1038/s41562-019-0813-1

Ponsot, E., Varnet, L., Wallaert, N., Daoud, E., Shamma, S., Lorenzi, C. & Neri, P. (2021). Mechanisms of Spectrotemporal Modulation Detection for Normal- and Hearing-Impaired Listeners. Trends Hear, 25, 2331216520978029. doi:10.1177/2331216520978029

Ponsot, E., Burred, J., Belin, P. & Aucouturier, J. (2018). Reply to Knight et al.: The complexity of inferences from speech prosody should be addressed using data-driven approaches. Proceedings of the National Academy of Sciences of the United States of America, 115(27), E6104-E6105. doi:10.1073/pnas.1806857115