Mathematics of Neural Circuits


Research center

45 rue d’Ulm
75230 Paris
Marc Mézard


Ecole Normale Supérieure


Laboratoire de Neurosciences Cognitives


Synaptic plasticity
computational neuroscience
cognitive decision processes
neuronal architectures
drug addiction
short-term memory


Buchin A, Chizhov A, Huberfeld G, Miles R, Gutkin BS. Reduced Efficacy of the KCC2 Cotransporter Promotes Epileptic Oscillations in a Subiculum Network Model. J Neurosci. 2016 Nov 16;36(46):11619-11633.

Keramati M, Gutkin B. Homeostatic reinforcement learning for integrating reward collection and physiological stability. Elife. 2014 Dec 2;3. doi: 10.7554/eLife.04811.

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

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., Pages, 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, in press, (2012).

DiPoppa, M., Krupa, M., Torcini, A., and Gutkin, B., Marginally Stable States and Quasi-periodic minor attractors in excitable pulse-coupled networks, SIAM Journal of Applied 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).

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

Fields of research

Computational neurosciences / neural theory

Research Theme

The goal of the GNT is to understand the basis of information processing in the brain by identifying links between collective neural dynamics and function. Research in the group comprises a broad spectrum of different topics in Computational Neuroscience, including network models of working memory, drug addiction models, probabilistic inference, feature integration, and statistical learning in neuronal architectures, spike-based learning algorithms, and mean-field analysis of recurrent networks, modeling of short-term plasticity in synaptic transmission and dynamics of GABA neurotransmission.

The work is based on methods form computational neuroscience, mathematics, statistics, computer science and physics: e.g. dynamical systems, Bayesian statistics, machine learning, statistical data analysis, stochastic differential equations, compartmental modelling, and mean field methods.

The interns will have a opportunity to take part in a wide range of research projects within the sub-teams of the three PIs: Boris Gutkin, Christian Machens and Sophie Deneve.  The Group for Neural Theory has was founded in 2005 as a part of the Départment d'Etudes Cognitives (DEC) at Ecole Normale Supérieure (ENS). The group is now part of the Laboratoire de Neurosciences Cognitives (LNC, INSERM Unité 960) within the DEC at the ENS.

ENP Students