Publications

Dumont, G., Ermentrout, G. & Gutkin, B. (2017). Macroscopic phase-resetting curves for spiking neural networks. Physical review. E, 96, 042311. doi:10.1103/PhysRevE.96.042311

Dumont, G. & Gutkin, B. (2019). Macroscopic phase resetting-curves determine oscillatory coherence and signal transfer in inter-coupled neural circuits. PLOS Computational Biology . doi:10.1371/journal.pcbi.1007019

Grèzes, J., Dezecache, G. & Eskenazi, T. (2015). Limbic to Motor Interactions during Social Perception. In Arthur W. Toga (Eds.), Brain Mapping: An Encyclopedic Reference (pp. 1027-1030).Academic Press: Elsevier

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

Buchin, A., Rieubland, S., Häusser, M., Gutkin, B. & Roth, A. (2016). Inverse Stochastic Resonance in Cerebellar Purkinje Cells. PLoS computational biology, 12(8), e1005000. doi:10.1371/journal.pcbi.1005000

Krupa, M., Gielen, S. & Gutkin, B. (2014). Intrinsic and synaptic mechanisms for clustered cortical gamma. J Comput. Neurosci , 37(2), 357-76

Marty, P., Chemla, E. & Spector, B. (2013). Interpreting numerals and scalar items under memory load. Lingua, 133, 152 - 163. doi:https://doi.org/10.1016/j.lingua.2013.03.006

Weiss, A., Chambon, V., Drugowitsch, J. & Wyart, V. (2021). Interacting with volatile environments stabilizes hidden-state inference and its brain signatures. Nature Communications, 12, 2228. doi:10.1038/s41467-021-22396-6

Tuckwell, H., Jost, J. & Gutkin, B. (2009). Inhibition and modulation of rhythmic neuronal spiking by noise. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 80(3). doi:10.1103/PhysRevE.80.031907

Chambon, V., Thero, H., Vidal, M., Vandendriessche, H., Haggard, P. & Palminteri, S. (2020). Information about action outcomes differentially affects learning from self-determined versus imposed choices. Nature Human Behaviour , 4, 1067-1079. doi:10.1038/s41562-020-0919-5

Chemla, E. & Schlenker, P. (2012). Incremental Vs. Symmetric Accounts of Presupposition Projection: An Experimental Approach. Natural Language Semantics, 20(2), 177–226 . doi:10.1007/s11050-012-9080-7

Canavier, C., Evans, R., Oster, A., Pissadaki, E., Drion, G., Kuznetsov, A. & Gutkin, B. (2016). Implications of cellular models of dopamine neurons for disease. Journal of neurophysiology, 116(6), 2815-2830. doi:10.1152/jn.00530.2016

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

Mascarenhas, S. & Koralus, P. (2017). Illusory inferences with quantifiers. Thinking and Reasoning, 23(1), 33–48. doi:10.1080/13546783.2016.1167125

Koralus, P. & Mascarenhas, S. (2018). Illusory Inferences in a question-based theory of reasoning. In Turner, Ken and Horn, Laurence (Eds.), Pragmatics, Truth, and Underspecification: Towards an Atlas of Meaning (pp. 300–322). Leiden: Brill

Schlenker, P. (2018). Iconic pragmatics. Natural Language and Linguistic Theory, 36(3), 877–936. doi:10.1007/s11049-017-9392-x

Grèzes, J. & Dezecache, G. (2014). How do shared-representations and emotional processes cooperate in response to social threat signals? Neuropsychologia, 55, 105-14. doi:10.1016/j.neuropsychologia.2013.09.019

Duriez, A., Bergerot, C., J Cone, J., F Roitman, M. & Gutkin, B. (2023). Homeostatic Reinforcement Theory Accounts for Sodium Appetitive State- and Taste-Dependent Dopamine Responding. Nutrients, 15(4), 1015. doi:10.3390/nu15041015

Keramati, M. & Gutkin, B. (2014). Homeostatic reinforcement learning for integrating reward collection and physiological stability. eLife, 3. doi:10.7554/eLife.04811

Gu, Z., Smith, K. , Loukatou, G., Completo Guerreiro, I., Dudek, S. , Gutkin, B., Jensen, P. & Yakel, J. (2020). Hippocampal Interneuronal α7 nAChRs Modulate Theta Oscillations in Freely Moving Mice . Cell Reports, 31(10), 107740. doi:10.1016/j.celrep.2020.107740

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

Cayco Gajic, A. & Zylberberg, A. (2021). Good decisions require more than information. Nature Neuroscience, 2', 903-904. doi:10.1038/s41593-021-00883-9

Schlenker, P. (2018). Gesture projection and cosuppositions. Linguistics and Philosophy, 41(3), 295–365. doi:10.1007/s10988-017-9225-8

Schlenker, P. & Chemla, E. (2017). Gestural agreement. Natural Language & Linguistic Theory, 36(2), 587–625. doi:10.1007/s11049-017-9378-8

Schlenker, P. & Chemla, E. (2018). Gestural agreement. Natural Language & , 36(2), 587-625. doi:10.1007/s11049-017-9378-8

Schlenker, P., Chemla, E., Schel, A., Fuller, J., Gautier, J., Kuhn, J., Veselinović, D., Arnold, K., Cäsar, C., Keenan, S., Lemasson, A., Ouattara, K., Ryder, R. & Zuberbühler, K. (2016). Formal monkey linguistics: The debate. Theoretical Linguistics, 42(1-2), 173-201. doi:10.1515/tl-2016-0010

Dipoppa, M. & Gutkin, B. (2013). Flexible frequency control of cortical oscillations enables computations required for working memory. Proceedings of the National Academy of Sciences of the United States of America, 110(31), 12828-12833. doi:10.1073/pnas.1303270110

Chemla, E. & Spector, B. (2011). Experimental Evidence for Embedded Scalar Implicatures. Journal of Semantics, 28(3), 359-400. doi:10.1093/jos/ffq023

Garcia, B., Lebreton, M., Bourgeois-Gironde, S. & Palminteri, S. (2023). Experiential values are underweighted in decisions involving symbolic options. Nature human behaviour, . doi:10.1038/s41562-022-01496-3

Gutkin, B., Braun, P., Akila, M., Waltner, D. & Guhr, T. (2020). Exact local correlations in kicked chains. Phys. Rev. B, 102, 174307. doi:10.1103/PhysRevB.102.174307