Publications

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

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

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

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

Maex, R., Budygin, E., Grinevich, V., Bencherif, M. & Gutkin, B. (2014). Receptor activation and desensitization as mechanisms for a7 regulation of dopamine response to nicotine. Chemical Neuroscience , 15(10), 1032-40. doi:10.1021/cn500126t

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

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, .

Dipoppa, M., Szwed, M. & Gutkin, B. (2016). Controlling Working Memory Operations by Selective Gating: The Roles of Oscillations and Synchrony. Advances in cognitive psychology, 12(4), 209-232. doi:10.5709/acp-0199-x

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

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

Morozova, E., Zakharov, D., Gutkin, B., Lapish, C. & Kuznetsov, A. (2016). Dopamine Neurons Change the Type of Excitability in Response to Stimuli. PLoS computational biology, 12(12), e1005233. doi:10.1371/journal.pcbi.1005233

Novikov, N. & Gutkin, B. (2016). Robustness of persistent spiking to partial synchronization in a minimal model of synaptically driven self-sustained activity. Physical review. E, 94(5-1), 052313. doi:10.1103/PhysRevE.94.052313

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

Buchin, A., Chizhov, A., Huberfeld, G., Miles, R. & Gutkin, B. (2016). Reduced Efficacy of the KCC2 Cotransporter Promotes Epileptic Oscillations in a Subiculum Network Model. The Journal of Neuroscience, 36(46), 11619--11633. doi:10.1523/JNEUROSCI.4228-15.2016

Morozova, E., Myroshnychenko, M., Zakharov, D., Di Volo, M., Gutkin, B., Lapish, C. & Kuznetsov, A. (2016). Contribution of synchronized GABAergic neurons to dopaminergic neuron firing and bursting. Journal of neurophysiology, 116(4), 1900-1923. doi:10.1152/jn.00232.2016

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

Maex, R. & Gutkin, B. (2017). Temporal integration and 1/f power scaling in a circuit model of cerebellar interneurons. Journal of neurophysiology, 118(1), 471-485. doi:10.1152/jn.00789.2016

Keramati, M., Durand, A., Girardeau, P., Gutkin, B. & Ahmed, S. (2017). Cocaine addiction as a homeostatic reinforcement learning disorder. Psychological review, 124(2), 130-153. doi:10.1037/rev0000046

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

Koukouli, F., Rooy, M., Tziotis, D., Sailor, K., O'Neill, H., Levenga, J., Witte, M., Nilges, M., Changeux, J., Hoeffer, C., Stitzel, J., Gutkin, B., Digregorio, D. & Maskos, U. (2017). Nicotine reverses hypofrontality in animal models of addiction and schizophrenia. Nature medicine, 23(3), 347-354. doi:10.1038/nm.4274

Lussange, J., Belianin, A., Bourgeois-Gironde, S. & Gutkin, B. (2018). A bright future for financial agent-based models. arxiv, nc

Stefanini, F. , A. Kheirbek, M. , Kushnir, L., C. Jimenez, J. , H. Jennings, J. , D. Stuber, G. , Hen, R. & Fusi, S. (2018). A distributed neural code in the dentate gyrus and in CA1. . doi:10.1101/292953

Kushnir, L. & Fusi, S. (2018). Neural Classifiers with Limited Connectivity and Recurrent Readouts. Journal of Neuroscience, 38 (46), 9900-9924. doi:10.1523/JNEUROSCI.3506-17.2018

Jacquet, P., Safra, L., Wyart, V., Baumard, N. & Chevallier, C. (2019). The ecological roots of human susceptibility to social influence: a pre-registered study investigating the impact of early-life adversity. Royal Society Open Science, 6(1), 180454. doi:10.1098/rsos.180454

Ioannou, C., Zein, M., Wyart, V., Scheid, I., Amsellem, F., Delorme, R., Chevallier, C. & Grèzes, J. (2017). Shared mechanism for emotion processing in adolescents with and without autism. Scientific reports, 7, 42696. doi:10.1038/srep42696

Correa, C. , Noorman, S. , Jiang, J. , Palminteri, S., Cohen, M. , Lebreton, M. & van Gaal, S. (2018). How the level of reward awareness changes the computational and electrophysiological signatures of reinforcement learning. Journal of Neuroscience , 0457-18. doi:10.1523/JNEUROSCI.0457-18.2018

Giavazzi, M., Daland, R., Palminteri, S., Peperkamp, S., Brugières, P., Jacquemot, C., Schramm, C., Cleret de Langavant, L. & Bachoud-Levi, A. (2018 ). The role of the striatum in linguistic selection: Evidence from Huntington’s disease and computational modeling. Cortex, 109, 189-204. doi:10.1016/j.cortex.2018.08.031

Bavard, S., Lebreton, M., Khamassi, M., Coricelli, G. & Palminteri, S. (2018). Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences. Nature Communications, 9(4503)