Research Director
Chef d’equipe INSERM "Mathematics of Neural Circuits" LNC2 ; Invited Professor, Higher School of Economics, Moscow Russia
Cognitive Studies
mathematical neuroscience, dynamics of neural circuits, dynamics of neural excitability, neural oscillations, reinforcement learning
Department of Cognitive Studies
, updated on
14 November 2021
Laboratoire de neurosciences cognitives et computationnelles
2ème étage, bureau C204A
29, rue d'Ulm 75230 Paris cedex 05
01 44 322956

My interests focus on computational and mathematical neuroscience. My research has focused on modelling brain circuit dynamics, neuromodulation, motivated behavior, addiction, machine learning.

Field of research

My work  bears on different themes in computational and mathematical neuroscience, the underlying
principle guiding my research is to use the tools of mathematical and computational analysis to
build mechanistic links across scales of neuronal organization: from the dynamics of brain cells, circuits and networks to cognitive function and to bring a coherent synthesis to the multitude of collected, and at times disparate,  data. 

Throughout my career, I have been particularly interested in neuromodulatory processes and how they shape (and misshape) neuronal dynamics, impacting neural systems functions and cognition.  I have been studying the influence of endogenous neuromodulators (e.g. Ach and DA) on neuronal dynamics and their role in cognition (working memory, decision making, learning) as well as exogenous substances that usurp and hijack brain system to produce large scale pathologies: e.g. addiction to drugs such as nicotine, cocaine, alcohol.

My research builds multi-scale mathematical models to analyze the dynamics and the functions of neural structures: from cells to circuits to cognition and behavior.

  • Linking single cell level to function I strive to understand the impact of neuromodulatory processes on the cellular excitability and its implication for circuit computations, such we grid cell formation
  • Linking the circuit level to large scale networks and cognitive function, I study the dynamics of collective behaviors that arise as a consequence of the cell dynamics: emergent synchronization, cortical signal processing/coding and the sustained activity in working memory.
  • Linking physiology to the behavioral level study the dynamics of motivated decision making learning and its relationship to homeostatic processes.

I am keenly interested in probing the mechanisms for brain disorders, notably the progression to addiction, schizophrenia and Alzheimer’s disease, focusing on nicotinic neuromodulation.


  • Rooy M, Lazarevich I, Koukouli F, Maskos U, Gutkin B.S. (2021) Cholinergic modulation of hierarchical inhibitory control over cortical resting state dynamics: Local circuit modeling of schizophrenia-related hypofrontality, Current Research in Neurobiology, 100018, ISSN 2665-945X, 10.1016/j.crneur.2021.100018
  • Zeldenrust F, Gutkin B, Denéve S (2021) Efficient and robust coding in heterogeneous recurrent networks. PLoS Comput Biol 17(4): e1008673.
  • Novikov, N and Gutkin B. (2020) Role of synaptic nonlinearity in persistent firing rate shifts caused by external periodic forcing. Phys. Rev. E 101(5): 052408}, doi:10.1103/PhysRevE.101.052408.
  • Romagnoni A, Colonese, M, Touboul J and Gutkin B (2020) Progressive Alignment of Inhibitory and Excitatory Delay May Drive a Rapid Developmental Switch in Cortical Network Dynamics, J. Neurophys, 123(5):1583-1599. doi: 10.1152/jn.00402.2019.
  • Lussange, J., Lazarevich, I., Bourgeois-Gironde, S. Palminteri, S. & Gutkin, B. (2020)  Modelling Stock Markets by Multi-agent Reinforcement Learning. Comput Econ. doi: 10.1007/s10614-020-10038-w
  • Gu Z., Smith K.G., Alexander G. M., Guerreiro I., Dudek S.M., Gutkin, B.,Jensen P. Yakkel J.L. (2020) Hippocampal interneuronal α7 nAChRs modulate theta oscillations in freely moving mice. Cell Reports 31, 107740. doi: 10.1016/j.celrep.2020.107740
  • Dumont G, Gutkin B (2019) Macroscopic phase resetting-curves determine oscillatory coherence and signal transfer in inter-coupled neural circuits. PLOS Computational Biology 15(5): e1007019.
  • Keramati M, Ahmed SH, Gutkin BS. (2017) Misdeed of the need: towards computational accounts of transition to addiction. Curr Opin Neurobiol. 8;46:142-153.
  • Koukouli F, Rooy M, Tziotis D, Sailor KA, O'Neill HC, Levenga J, Witte M, Nilges M, Changeux JP, Hoeffer CA, Stitzel JA, Gutkin BS, DiGregorio DA, Maskos U.. (2017) Nicotine reverses hypofrontality in animal models of addiction and schizophrenia. Nature Medicine doi: 10.1038/nm.4274.
  • Buchin A, Chizhov A, Huberfeld G, Miles R, Gutkin BS. (2016) Reduced Efficacy of the KCC2 Cotransporter Promotes Epileptic Oscillations in a Subiculum Network Model. J Neurosci. 36(46):11619-11633
  • Chalk M, Gutkin B, Denève S.(2016) Neural oscillations as a signature of efficient coding in the presence of synaptic delays, Elife. 2016 Jul 7;5. pii: e13824
  • Buchin A, Chizhov A, Huberfeld G, Miles R, Gutkin BS. (2016) Reduced Efficacy of the KCC2 Cotransporter Promotes Epileptic Oscillations in a Subiculum Network Model. J Neurosci. 36(46):11619-1163. doi: 0.1523/JNEUROSCI.4228-15.2016
  • A Hyafil, L Fontolan, AL Giraud,  B Gutkin (2015) Neural cross-frequency coupling: connecting architectures, mechanisms and functions. Trends in Neurosciences 8(11):725-40. doi: 10.1016/j.tins.2015.09.001
  • A Hyafil, L Fontolan, C Kabdebon, B Gutkin, AL Giraud (2015) Speech encoding by coupled cortical theta and gamma oscillations eLife, e06213
  • Keramati M, Gutkin B.S. (2015) Homeostatic reinforcement learning for integrating reward collection and physiological stability eLife doi: 10.7554/eLife.04811
  • Dipoppa M and Gutkin B.S. (2013) Flexible frequency control of cortical oscillations enables computations required for working memory PNAS 110(31):12828-33.
  • Remme M, Lengyel M, Gutkin B.S. 2010 Democracy-independence trade-off in oscillating dendrites and its implications for grid cells. Neuron 66(3):429-37.  
  • Gutkin BS, Dehaene S & Changeux J-P. 2006 Neuro-computational Hypotesis for Nicotine Addiction. PNAS 24:1106-11. doi: 10.1073/pnas.0510220103