Publications

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, .

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.)

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

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

Michel, M. , Beck, D. , .., .., King, J. , Kouider, S., Tallon-Baudry, C. & Yoshida , M. (2019). Opportunities and challenges for a maturing science of consciousness. Nature Human Behaviour, 3, 104-107. doi:10.1038/s41562-019-0531-8

Babo-Rebelo, M., Buot, A. & Tallon-Baudry, C. (2019). Neural responses to heartbeats distinguish self from other during imagination. NeuroImage, 191, 10-20. doi:10.1016/j.neuroimage.2019.02.012

Tallon-Baudry, C., Florence, C., Hyeong-Dong, P. & Babo-Rebelo, M. (2018). The neural monitoring of visceral inputs, rather than attention, accounts for first-person perspective in conscious vision. Cortex, . doi:10.1016/j.cortex.2017.05.019

Babo-Rebelo, M., Richter, C. & Tallon-Baudry, C. (2016). Neural Responses to Heartbeats in the Default Network Encode the Self in Spontaneous Thoughts. The Journal of Neuroscience, 36(30), 7829-7840. doi:10.1523/JNEUROSCI.0262-16.2016

Babo-Rebelo, M., Wolpert, N., Adam, C., Hasboun, D. & Tallon-Baudry, C. (2016). Is the cardiac monitoring function related to the self in both the default network and right anterior insula? Philosophical Transactions B, 371(1708), 20160004. doi:10.1098/rstb.2016.0004

Rebollo, I., Devauchelle, A., D., Béranger, B. & Tallon-Baudry, C. (2018). Stomach-brain synchrony reveals a novel, delayed-connectivity resting-state network in humans. eLife, 7, e33321. doi:10.7554/eLife.33321