Publications

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

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

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

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

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

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

Lanore, F. , Cayco Gajic, A., Gurnani, H. , Coyle, D. & Angus Silver, R. (2021). Cerebellar granule cell axons support high-dimensional representations. Nature Neuroscience, 24, 1142-1150. doi:10.1038/s41593-021-00873-x

Lazarevich, I. , Stasenko , S., Rozhnova , M., Pankratova , E., Dityatev, A. & et al., .. (2020). Activity-dependent switches between dynamic regimes of extracellular matrix expression. PLoS ONE, 15(1). doi:10.1371/journal.pone.0227917

Lazarevich, I. , Gutkin, B. & Prokin, I. (2018). Neural activity classification with machine learning models trained on interspike interval series data. arxiv , 1810.03855

Lebreton, M., Bacily , K., Palminteri, S. & Engelmann, J. (2019). Contextual influence on confidence judgments in human reinforcement learning. PLoS Comput Biol, 15(4), e1006973. doi:10.1371/journal.pcbi.1006973

Lebreton, M., Bavard, S., Daunizeau, J. & Palminteri, S. (2019). Assessing inter-individual differences with task-related functional neuroimaging. Nature Human Behaviour. doi:10.1038/s41562-019-0681-8

Lebreton, M. & Palminteri, S. (2016). When are inter-individual brain-behavior correlations informative? bioRxiv. doi:10.1101/036772

Lefebvre, G., Nioche, A., Bourgeois-Gironde, S. & Palminteri, S. (2018). Contrasting temporal difference and opportunity cost reinforcement learning in an empirical money-emergence paradigm. PNAS, 115(49), E11446-E11454. doi:10.1073/pnas.1813197115

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

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

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

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

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

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

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

Martinez-Saito, M. , Konovalov, R. , Piradov, M. , Shestakova, A. , Gutkin, B. & Klucharev, V. (2018). Action in auctions: neural and computational mechanisms of bidding behavior. BioRxiv, 464925. doi:10.1101/464925

Mcdonnell, M., Iannella, N., To, M., Tuckwell, H., Jost, J., Gutkin, B. & Ward, L. (2015). A review of methods for identifying stochastic resonance in simulations of single neuron models. Network: Computation in Neural Systems, 26(2), 35-71. doi:10.3109/0954898X.2014.990064

Morozova, E., Faure, P., Gutkin, B., Lapish, C. & Kuznetsov, A. (2020). Distinct Temporal Structure of Nicotinic ACh Receptor Activation Determines Responses of VTA Neurons to Endogenous ACh and Nicotine. eNeuro, 7(4). doi:10.1523/ENEURO.0418-19.2020

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

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

Muller, L., Brette, R. & Gutkin, B. (2011). Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike phase-locking. Frontiers in Computational Neuroscience, 15(5), 45. doi:10.3389/fncom.2011.00045

Nair, A. , B. Johnson, E. , Gregory, S. , Osborne-Crowley, K. , Zeun, P. , Scahill, R. , Lowe, J. , Papoutsi, M., Palminteri, S., Rutledge, R. , Rees, G. & Tabrizi, S. (2021). Aberrant Striatal Value Representation in Huntington's Disease Gene Carriers 25 Years Before Onset. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(9), 910-918. doi:10.1016/j.bpsc.2020.12.015

Najar, A., Bonnet, E., Bahador, B. & Palminteri, S. (2020). The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning. PLoS Biology, 18 (12), e3001028. doi:10.1371/journal.pbio.3001028