Publications

Caze, R., Humphries, M. & Gutkin, B. (2012). Spiking and saturating dendrites differentially expand single neuron computation capacity. , Vol. 13: In Twenty First Annual Computational Neuroscience Meeting: CNS*2012, Decatur, GA, USA.

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

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

Remme, M., Lengyel, M. & Gutkin, B. (2009). The role of ongoing dendritic oscillations in single-neuron dynamics. PLOS Comp. Bio. , 5(9)

Gutkin, B., Tuckwell, H. & Jost, J. (2009). The Phenomenon of Inverse Stochastic Resonance. Naturwissenschaften, 96(9), 1091-7

Graupner, M., Ahmed, S. & Gutkin, B. (2009). Modelling dynamics of addiction. Pharmacopsychiatry, 42(Suppl 1), S144-52

Stiefel, K., Gutkin, B. & Sejnowski, T. (2009). Simulation of effects of cholinergic modulation of phase response curves. J Comp Neurosci, 29(2)

Gutkin, B. & Ahmed, S. (2012). Computational Neuroscience of Drug Addiction.

Kuznetsov, A. & Gutkin, B. (2015). Dopaminergic cell Models. The Encyclopedia of Computational Neuroscience (pp. 2958-2965).

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

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

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

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

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.)

Gutkin, B. & Stiefel, K. (2007). Phase-resetting curves and neuromodulation of action potential dynamics in the cortex. (Vol. 40, pp. 14-15).

Palminteri, S., Wyart, V. & Koechlin, E. (2017). The Importance of Falsification in Computational Cognitive Modeling. Trends in Cognitive Sciences, 21(6), 425-433. doi:10.1016/j.tics.2017.03.011

Hertz, U., Palminteri, S., Brunetti, S., Olesen, C., Frith, C. & Bahrami, B. (2017). Neural computations underpinning the strategic management of influence in advice giving. Nature communications, 8(1), 2191. doi:10.1038/s41467-017-02314-5

Palminteri, S., Lefebvre, G., Kilford, E. & Blakemore, S. (2017). Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing. PLoS computational biology, 13(8), e1005684. doi:10.1371/journal.pcbi.1005684

Salvador, A., Worbe, Y., Delorme, C., Coricelli, G., Gaillard, R., Robbins, T., Hartmann, A. & Palminteri, S. (2017). Specific effect of a dopamine partial agonist on counterfactual learning: evidence from Gilles de la Tourette syndrome. Scientific reports, 7(1), 6292. doi:10.1038/s41598-017-06547-8

Palminteri, S. & Pessiglione, M. (2016). Opponent brain systems for reward and punishment learning: Causal evidence from drug and lesion studies in humans. Decision Neuroscience: An Integrative Perspective, 291-303. doi:10.1016/B978-0-12-805308-9.00023-3

Palminteri, S., Kilford, E., Coricelli, G. & Blakemore, S. (2016). The Computational Development of Reinforcement Learning during Adolescence. PLoS Computational Biology, 12(6). doi:10.1371/journal.pcbi.1004953

Delorme, C., Salvador, A., Valabrègue, R., Roze, E., Palminteri, S., Vidailhet, M., De Wit, S., Robbins, T., Hartmann, A. & Worbe, Y. (2016). Enhanced habit formation in Gilles de la Tourette syndrome. Brain : a journal of neurology, 139(Pt 2), 605-15. doi:10.1093/brain/awv307

Delorme, C., Salvador, A., Valabrègue, R., Roze, E., Palminteri, S., Vidailhet, M., De Wit, S., Robbins, T., Hartmann, A. & Worbe, Y. (2015). Enhanced habit formation in Gilles de la Tourette syndrome. Brain, 139(2), 605-615. doi:10.1093/brain/awv307

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

Lefebvre, G., Lebreton, M., Meyniel, F., Bourgeois-Gironde, S. & Palminteri, S. (2017). Behavioural and neural characterization of optimistic reinforcement learning. Nature Human Behaviour , 1(4), 0067

Dumont, G., Payeur , A. & Longtin , A. (2017). A stochastic-field description of finite-size spiking neural networks. PLoS Comput Biol, 13(8), e1005691. doi:10.1371/journal.pcbi.1005691

Csaba Farkas, B. , Baptista, A., Speranza, M., Wyart, V. & Jacquet, P. (2024). Specifying the timescale of early life unpredictability helps explain the development of internalising and externalising behaviours. Scientific reports, 14(1), 3563. doi:10.1038/s41598-024-54093-x

Balsdon, T., Wyart, V. & Mamassian, P. (2024). Metacognitive evaluation of postdecisional perceptual representations. Journal of vision, 24(4), 2. doi:10.1167/jov.24.4.2

Lunven, M., Hernandez Dominguez, K., Youssov, K., Hamet Bagnou, J., Fliss, R. , Vandendriessche, H., Bapst, B. , Morgado, G., Remy, P., Schubert, R. , Reilmann, R., Busse, M., Craufurd, D., Massart, R., Rosser, A. & Bachoud-Levi, A. (2023). A new approach to digitized cognitive monitoring: validity of the SelfCog in Huntington's disease. Brain communications, 5(2), fcad043. doi:10.1093/braincomms/fcad043

Gurnani, H. & Cayco Gajic, A. (2023). Signatures of task learning in neural representations. Signatures of task learning in neural representations, Current Opinion in Neurobiology,, 83, 102759. doi:10.1016/j.conb.2023.102759