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List of the GNT publications


Buchin  A,  Rieubland  S, Häusser M, Gutkin B.S. & Roth A (2016)  Inverse stochastic resonance in cerebellar Purkinje cells. PLOS Comput. Biology, in press

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, DOI:10.3389/fncom.2016.00048



Hyafil A, Giraud AL, Fontolan L, Gutkin B. Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions. Trends Neurosci. 2015 Nov;38(11):725-40

Oster A, Faure P, Gutkin BS.Mechanisms for multiple activity modes of VTA dopamine neurons. Front Comput Neurosci. 2015 Jul 28;9:95

Hyafil A, Fontolan L, Kabdebon C, Gutkin B, Giraud AL.Speech encoding by coupled cortical theta and gamma oscillations.Elife. 2015 May 29;4:e06213.

Tran-Van-Minh A, Cazé RD, Abrahamsson T, Cathala L, Gutkin BS, DiGregorio DA.Contribution of sublinear and supralinear dendritic integration to neuronal computations.Front Cell Neurosci. 2015 Mar 24;9:67.


Maex R, Grinevich VP, Grinevich V, Budygin E, Bencherif M, Gutkin B.Understanding the role α7 nicotinic receptors play in dopamine efflux in nucleus accumbens. ACS Chem Neurosci. 2014 Oct 15;5(10):1032-40

McDonnell MD, Iannella N, To MS, Tuckwell HC, Jost J, Gutkin BS, Ward LM. A review of methods for identifying stochastic resonance in simulations of single neuron models. Network. 2015;26(2):35-71.

Keramati M, Gutkin B.Homeostatic reinforcement learning for integrating reward collection and physiological stability. Elife. 2014 Dec 2;3.

Savin C., Deneve S. Spatio-temporal Representations of Uncertainty in Spiking Neural Networks, Advances in Neural Information Processing Systems, 2024-2032

Ostojic, S., Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons, Nature Neuroscience, 17(4), p.594 - 600 (2014).

Gutkin, B.S. and Zeldenrust F., Spike Frequency Adaptation, Scholarpedia, 9(2):30643. (2014)

Schwemmer M.A., Fairhall A.L., Deneve S., Shea-Brown E.T., Constructing precisely computing networks with biophysical spiking neurons. arXiv:1411.3191

Notredame C.E., Pins D., Deneve S., Jardri R., What visual illusions teach us about schizophrenia., Front Integr Neurosci. 12;8:63 (2014).

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

Gutkin, B.S., Theta-Neuron Model, Encyclopedia of Comptutational Neuroscience, Springer, p 1-9 (2014).


Wohrer, A. and Machens, C.K., Percept and the single neuron (News and Views on Haefner et al. 2013, same issue), Nature Neuroscience, 16, 112-113 (2013).

Caze, R.D., Humphries, M., and Gutkin, B.S., Passive Dendrites Enable Single Neurons to Compute Linearly Non-separable Functions, PLOS Computational Biology, 9(2): e1002867, (2013).

Jardri R, Deneve S., Circular inferences in schizophrenia. Brain, 136-11 (2013).

DG Barrett, S Deneve, CK Machens. Firing rate predictions in optimal balanced networks. Advances in Neural Information Processing Systems, 1538-1546 (2013).

Boerlin M, Machens CK, Deneve S., Predictive coding of dynamical variablesin balanced spiking networks. PLoS Comput Biol, 9(11) (2013).

Keramati, M. and Gutkin, B.S., Imbalanced decision hierarchy in addicts emerging from drug-hijacked dopamine spiraling circuit, PLOS One, 8:4, 1-8 (2013).  

Dipoppa, M. and Gutkin, B.S., Flexible frequency control of cortical oscillations enables computations required for working memory., PNAS, vol. 110 no. 31 (2013). 

Graupner, M., Maex, R., and Gutkin, B.S., Endogenous cholinergic inputs and local circuit mechanisms govern the phasic mesolimbic dopamine response to nicotine, PLoS Computational Biology 9(8): e1003183, (2013).

Fontolan, L., Krupa, M.P., Hyafil, A., and Gutkin, B., Analytical insights on theta-gamma coupled neural oscillators, The Journal of Mathematical Neuroscience, 3:16 (2013).  

Wu, J., Gaol, M., Shen, J., Shi, W., Oster, A.M., and Gutkin, B.S., Cortical control of VTA function and influence on nicotine reward, Biochemical Pharmacology, v. 86(8), p.1173-1180 (2013).

Dipoppa, M. and Gutkin, B.S., Correlations in background activity control persistent state stability and allow execution of working memory tasks., Frontiers in Computational Neuroscience, 7:00139, (2013).

Ostojic, S. and Fusi, S., Synaptic encoding of temporal contiguity, Front. Comput. Neurosci. 7:32 (2013).

E.S. Schaffer, S. Ostojic and L.F. Abbott, A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks, Plos Comput Biol, 9(10): e1003301. (2013).


Lochmann, T., Ernst, U.A., and Denève, S., Perceptual inference predicts contextual modulations of sensory responses., Journal of Neuroscience, 32(12), 4179-95 (2012).

Tolu, S., Eddine, R., Marti, F., David, V., Graupner, M., Baudonnat, S.P.M., Besson, M., Reperant, C., Zemdegs, J., Pag s, C., Caboche, J., Gutkin, B., Gardier, A.M., Changeux, J., Faure, P., and Maskos, U., Co-activation of VTA DA and GABA neurons mediates nicotine reinforcement., Molecular Psychiatry, 18(3):382-93, (2012).
DiPoppa, M., Krupa, M., Torcini, A., and Gutkin, B., Splay States in Finite Pulse-Coupled Networks of Excitable Neurons, SIAM Journal of Appllied Dynamical Systems, 11, 864 894 (2012).

Deneve, S., Making decisions with unknown sensory reliability., Frontiers in Neuroscience, 6:75, doi: 10.3389/fnins.2012.00075 (2012).  

Barrett, D., Machens C., Deneve S., Learning optimal spike-based
. Advances in Neural Information Processing Systems 25, 2294-2302 (2012)

Jardri, R. and Deneve, S., Computational models of hallucinations., The Neuroscience of Hallucinations, pp 289-313 (2012).

Zhang, D., Gao, M., Xu, D., Shi, W., Gutkin, B., Steffensen, S., Lukas, R., and Wu, J., Impact of prefrontal cortex in nicotine-induced excitation of VTA dopamine neurons in anesthetized rats, Journal of Neuroscience, 32(36) (2012).

Caze, R., Humphries, M., and Gutkin, B.S., Spiking and saturating dendrites differentially expand single neuron computation capacity, Advances in Neural Information Processing, (2012).

Wohrer, A., Humphries, M.D., and Machens, C.K., Population-wide distributions of neural activity during perceptual decision-making, Progress in Neurobiology, http://dx.doi.org/10.1016/j.pneurobio.2012.09.004 (online publication), (2012).


Keramati, M., Dezfouli, A., and Piray, P., Understanding Addiction as a Pathological State of Multiple Decision Making Processes: A Neurocomputational Perspective, in: Computational Neuroscience of Drug Addiction, eds. Gutkin, B. and Ahmed, S., (2011).
Keramati, M., Dezfouli, A., and Piray, P., Speed/Accuracy Trade-off between the Habitual and the Goal-directed Processes, PLOS Comput Bio, 7:5, 1-25 (2011).

Humphries, M.D., Spike-train communities: finding groups of similar spike trains, Journal of Neuroscience, 31, 2321-2336 (2011).

Fischer, B.J. and Pena, J.L., Owl's behavior and neural representation predicted by Bayesian inference., Nature Neuroscience, 14, 1061-1066 (2011).

Muller, L., Brette, R., and Gutkin, B.S., Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike phase-locking., Frontiers in Neuroscience, in press, (2011).

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

Oster, A. and Gutkin, B.S., A reduced model of DA neuronal dynamics that displays quiescence, tonic firing and bursting., J Phyisiol (Paris), in press, (2011).

Gutkin, B.S. and Ahmed, S.H., Computational Neuroscience of Drug Addiction, in: , Springer Series in Computational Neuroscience, Springer Verlag, 10 DOI: 10.1007/978-1-4614-0751-5, (2011).
Graupner, M. and Gutkin, B.S., Modelling Local Circuit Mechanisms for Nicotine Control of Dopamine Activity, in: Computational Neuroscience of Drug Addiction, eds. Gutkin, B.S. and Ahmed, S.H., Computational Neuroscience Series, Springer Verlag, 10, 111-144 (2011).

Fischer, B.J., Steinberg, L.J., Fontaine, B., Brette, R., and Pena, J.L., Effect of instantaneous frequency glides on interaural time difference processing by auditory coincidence detectors, Proceedings of the National Academy of Sciences USA, 108, 18138-18143 (2011).

Boerlin, M. and Denève, S., Spike-Based Population Coding and Working Memory, PLoS Comput. Biol., 7(2), (2011). 

Lochmann, T. and Deneve, S., Neural processing as causal inference., Current Opinion in Neurobiology, 21(5), 774-81 (2011).  

Morel, P., Deneve, S., and Baraduc, P., Optimal and suboptimal use of postsaccadic vision in sequences of saccades., Journal of Neuroscience, 31(27), 10039-49 (2011).

Wardak, C., Deneve, S., and Hamed, S.B., Focused visual attention distorts distance perception away from the attentional locus., Neuropsychologia, 49(3), 535-45 (2011).

Lochmann, T. and Deneve, S., Optimal cue combination predict contextual effects on sensory neural responses., Sensory Cue Integration, (2011).


Machens, C.K., Romo, R., and Brody, C.D., Functional, But Not Anatomical, Separation of "What" and "When" in Prefrontal Cortex, Journal of Neuroscience, 30(1), 350-360 (2010)

Jedlicka, P., Deller, T., Gutkin, B.S., and Backus, K.H., Activity-Dependent Intracellular Chloride Accumulation and Diffusion Controls GABAA Receptor-Mediated Synaptic Transmission, Hippocampus, (2010).  

Remme, M., Lengyel, M., and Gutkin, B.S., Democracy-Independence Trade-Off in Oscillating Dendrites and Its Implications for Grid Cells, Neuron, 66, 429-437 (2010).

Weber, F., Machens, C.K., and Borst, A., Spatio-Temporal Response Properties of Optic-Flow Processing Neurons, Neuron, 67, 629-642 (2010).

Singheiser, M., Fischer, B.J., and Wagner, H., Estimated cochlear delays in low best-frequency neurons in the barn owl cannot explain coding of interaural time difference., J Neurophysiol, 104, 1946-1954 (2010).

Humphries, M.D. and Prescott, T.J., The ventral basal ganglia, a selection mechanism at the crossroads of space, strategy, and reward, Progress in Neurobiology, 90, 385-417 (2010).
Humphries, M.D., Wood, R., and Gurney, K., Reconstructing the three-dimensional GABAergic microcircuit of the striatum, PLoS Computational Biology, 6, e1001011 (2010).

Piray, P., Keramati, M., Dezfouli, A., Lucas, C., and Mokri, A., Individual Differences in Nucleus Accumbens Dopamine Receptors Predict Development of Addiction-like Behavior: A Computational Approach, Neural Computation, 22, 2334-2368 (2010).

Wohrer, A., Romo, R., and Machens, C.K., Linear readout from a neural population with partial correlation data, Advances in Neural Information Processing Systems, 23, 2469-2477 (2010).

Machens, C.K., Demixing population activity in higher cortical areas, Front Comput Neurosci, 4, 26 (2010).


Stiefel, K.M., Gutkin, B.S., and Sejnowski, T.E., The effects of cholinergic neuromodulation on neuronal phase-response curves of modeled cortical neurons, J Comput Neurosci, 29.2, 289-301 (2009).

Munuera, J., Morel, P., Duhamel, J., and Denève, S., Optimal sensorimotor control in eye movement sequences, Journal of Neuroscience, 29, 3026-35 (2009).

Priesemann, V., Munk, M., and Wibral, M., Subsampling effects in neuronal avalanche distributions recorded in vivo, BMC Neuroscience, 10:40, doi:10.1186/1471-2202-10-40 (2009).

Graupner, M. and Gutkin, B., Modeling nicotinic neuromodulation from global functional and network levels to nAChR based mechanisms, Acta Pharmacol Sin, 30(6), 681–6 (2009).
Ahmed, S.H., Graupner, M., and Gutkin, B., Computational Approaches to the Neurobiology of Drug Addiction, Pharmacopsychiatry, 42(Suppl. 1), S144-S152 (2009).  

Gutkin, B.S., Tuckwell, H., and Jost, J., The Phenomenon of Inverse Stochastic Resonance, Naturwissenschaft, DOI 10.1007/s00114-009-0570-5, (2009).

Remme, M., Lengyel, M., and Gutkin, B.S., The role of ongoing dendritic oscillations in single-neuron dynamics, PLOS Comput. Biol., 5(9), e1000493 (2009).
Tuckwell, H., Jost, J., and Gutkin, B.S., Inhibition and modulation of rhythmic neuronal spiking by noise, Physical Review E, 80, 031907 (2009).

Humphries, M.D., Lepora, N., Wood, R., and Gurney, K., Capturing dopaminergic modulation and bimodal membrane behaviour of striatal medium spiny neurons in accurate, reduced models, Frontiers in Computational Neuroscience, 3, 26 (2009).

Dezfouli, A., Piray, P., Keramati, M., Ekhtiari, H., Lucas, C., and Mokri, A., A Neurocomputational Model for Cocaine Addiction, Neural Computation, 21, 2869-2893 (2009).

Deneve, S., Bayesian approach to decision making., Handbook of reward and Decision making, (2009).


Denève, S., Bayesian Spiking Neurons I: Inference, Neural Computation, 20, 91-117 (2008).

Denève, S., Bayesian Spiking Neurons II: Learning, Neural Computation, 20, 118-145 (2008).
Mongillo, G. and Denève, S., Online Learning with Hidden Markov Models, Neural Computation, 20(7), 1706-16 (2008).

Gutkin, B.S., Tuckwell, H., and Jost, J., Random perturbations of spiking activity in a pair of coupled neurons, Theory in the Biosciences, (in press), (2008).

Gutkin, B.S., Tuckwell, H., and Jost, J., Transient termination of synaptically sustained firing by noise, Euro Physics Letters, 81, 20005 (2008).

Mongillo, G., Barak, O., and Tsodyks, M., Synaptic theory of working memory, Science, 319, 1543-1546 (2008).

Lochmann, T. and Denève, S., Information transmission with spiking Bayesian neurons, New Journal of Physics, 10, article ID: 055019 (2008).

Stiefel, K.M., Gutkin, B., and TE, T.E.S., Cholinergic modulation of dynamics underlying spike generation in cortical neurons, PLoS ONE, 3(12), e3947 (2008).


Jeong, H.Y. and Gutkin, B.S., Synchrony of neuronal oscillations controlled by GABAergic reversal potentials, Neural Computation, 19 (3), 706-729 (2007).
Ahmed, S., Bobashev, G., and Gutkin, B.S., The simulation of addiction: pharmacological and neurocomputational models of drug self-administration, Drug Alcohol Depend, 90(2-3), 304-11 (2007).
Bobashev, G., Costenbader, E., and Gutkin, B.S., Comprehensive mathematical modeling in drug addiction sciences, Drug Alcohol Depend, 89(1), 102-6 (2007).
Brumberg, J.C. and Gutkin, B.S., Cortical pyramidal cells as non-linear oscillators: Experiment and spike-generation theory, Brain Research, 1171, 122-137 (2007).

Denève, S., Duhamel, J., and Pouget, A., Optimal sensorimotor integration in recurrent cortical networks: a neural implementation of Kalman filters, Journal of Neuroscience, 27, 5744-5756 (2007).

Rouger, J., Lagleyre, S., Fraysse, B., Denève, S., Deguine, O., and Barone, P., Evidence that cochlear-implanted deaf patients are better multisensory integrators., Proceedings of the National Academy of Sciences USA, 104(17), 7295-7300. (2007).


Gutkin, B.S. and Ermentrout, G.B., Neuroscience: spikes too kinky in the cortex?, Nature, 440 (7087), 999-1000 (2006).

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