Publications

Charnavel , I. & Chemla, E. (2017). More Hybrid Agreement: simultaneous agreement with two competing triggers. Snippets, 31, 7-8. doi:10.7358/snip-2017-031-char

Lourenço, J. , De Stasi, A., Deleuze, C. , Bigot, M. , Pazienti, A. , Aguirre, A. , Giugliano, M., Ostojic, S. & Bacci, A. (2020). Modulation of Coordinated Activity across Cortical Layers by Plasticity of Inhibitory Synapses. Cell reports, 30(3), 630-641.e5. doi:10.1016/j.celrep.2019.12.052

Lourenço, J. , Michela De Stasi,, A. , Deleuze, C. , Bigot, M. , Pazienti, A. , Aguirre, A. , Giugliano, M. , Ostojic, S. & Bacci, A. (2019). Modulation of coordinated activity across cortical layers by plasticity of inhibitory synapses onto layer 5 pyramidal neurons. Cell Reports, 3, 630-641.e5. doi:10.1016/j.celrep.2019.12.052

Caze, R., Humphries, M. & Gutkin, B. (2013). Modulation of computational capacity of neurons due to dendritic synaptic interactions. PLoS Comput. Biol., 9(2)

Lussange, J., Lazarevich, I. , Bourgeois-Gironde, S., Palminteri, S. & Gutkin, B. (2020). Modelling Stock Markets by Multi-agent Reinforcement Learning. Computational Economics, 57, 113-147. doi:10.1007/s10614-020-10038-w

Krupa, M., Gutkin, B. & Zakharov, D. (2020). Modeling dopaminergic modulation of clustered gamma rhythms. Communications in Nonlinear Science and Numerical Simulation, 82(105086), 1-10. doi:10.1016/j.cnsns.2019.105086

Keramati, M., Ahmed, S. & Gutkin, B. (2017). Misdeed of the need: towards computational accounts of transition to addiction. Current opinion in neurobiology, 46, 142-153. doi:10.1016/j.conb.2017.08.014

Deperrois, N., Moiseeva, V. & Gutkin, B. (2019). Minimal Circuit Model of Reward Prediction Error Computations and Effects of Nicotinic Modulations. Frontiers in neural circuits, 12, 116. doi:10.3389/fncir.2018.00116

Oster, A., Faure, P. & Gutkin, B. (2015). Mechanisms for multiple activity modes of VTA dopamine neurons. Frontiers in computational neuroscience, 9, 95. doi:10.3389/fncom.2015.00095

Dipoppa, M., Krupa, M., Torcini, A. & Gutkin, B. (2012). Marginally Stable States and Quasi-periodic minor attractors in excitable pulse-coupled networks. SIADS , 63(1), 62-97

Maldonado, M., Chemla, E. & Dunbar, E. (2019). Manipulated decision tasks to decode behavioral measures: the case of mouse-tracking. Behavior Research Methods, TBA

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

Brendel, W. , Bourdoukan, R., Vertechi, P. , Machens, C. & Denève, S. (2020). Learning to represent signals spike by spike. PLOS Computational Biology, 16(3), e1007692. doi:10.1371/journal.pcbi.1007692

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

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

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

Denic, M., Chemla, E. & Tieu, L. (2018). Intervention effects in NPI licensing: a quantitative assessment of the scalar implicature explanation. Glossa: a journal of general linguistics, 3(1), 49. doi:10.5334/gjgl.388

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

Ladenbauer, J., McKenzie , S., English , D., Hagens , O. & Ostojic, S. (2019). Inferring and validating mechanistic models of neural microcircuits based on spike-train data. Nature Communications, 10(4933). doi:10.1038/s41467-019-12572-0

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

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