Nicolas Brunel

Professor of Neurobiology

Office Location: 
311 Research Drive, Durham, NC 27710
(919) 684-8684


We use theoretical models of brain systems to investigate how they process and learn information from their inputs. Our current work focuses on the mechanisms of learning and memory, from the synapse to the network level, in collaboration with various experimental groups. Using methods from
statistical physics, we have shown recently that the synaptic
connectivity of a network that maximizes storage capacity reproduces
two key experimentally observed features: low connection probability
and strong overrepresentation of bidirectionnally connected pairs of
neurons. We have also inferred `synaptic plasticity rules' (a
mathematical description of how synaptic strength depends on the
activity of pre and post-synaptic neurons) from data, and shown that
networks endowed with a plasticity rule inferred from data have a
storage capacity that is close to the optimal bound.

Education & Training

  • Ph.D., Pierre and Marie Curie University (France) 1993

Selected Grants

Learning spatio-temporal statistics from the environment in recurrent networks awarded by Office of Naval Research (Principal Investigator). 2017 to 2019

Brunel, N, and Hakim, V. "Population Density Models." Encyclopedia of Computational Neuroscience. Ed. D Jaeger and R Jung. Springer, 2014. Full Text

Brunel, N, and Hakim, V. "Fokker-Planck Equation." Encyclopedia of Computational Neuroscience. Ed. D Jaeger and R Jung. Springer, 2014. Full Text

Brunel, N, and Hakim, V. "Neuronal Dynamics." Encyclopedia of Complexity and Systems Science. Ed. RA Meyers. Springer, 2009. 6099-6116. Full Text

Tartaglia, EM, and Brunel, N. "Bistability and up/down state alternations in inhibition-dominated randomly connected networks of LIF neurons." Scientific reports 7.1 (September 20, 2017): 11916-. Full Text

Titley, HK, Brunel, N, and Hansel, C. "Toward a Neurocentric View of Learning." Neuron 95.1 (July 2017): 19-32. (Review) Full Text

Zampini, V, Liu, JK, Diana, MA, Maldonado, PP, Brunel, N, and Dieudonné, S. "Mechanisms and functional roles of glutamatergic synapse diversity in a cerebellar circuit." eLife 5 (September 19, 2016). Full Text Open Access Copy

Brunel, N. "Is cortical connectivity optimized for storing information?." Nature neuroscience 19.5 (May 2016): 749-755. Full Text

De Pittà, M, Brunel, N, and Volterra, A. "Astrocytes: Orchestrating synaptic plasticity?." Neuroscience 323 (May 2016): 43-61. (Review) Full Text

Dubreuil, AM, and Brunel, N. "Storing structured sparse memories in a multi-modular cortical network model." Journal of computational neuroscience 40.2 (April 2016): 157-175. Full Text

Bouvier, G, Higgins, D, Spolidoro, M, Carrel, D, Mathieu, B, Léna, C, Dieudonné, S, Barbour, B, Brunel, N, and Casado, M. "Burst-Dependent Bidirectional Plasticity in the Cerebellum Is Driven by Presynaptic NMDA Receptors." Cell reports 15.1 (April 2016): 104-116. Full Text

De Pittà, M, and Brunel, N. "Modulation of Synaptic Plasticity by Glutamatergic Gliotransmission: A Modeling Study." Neural plasticity 2016 (January 2016): 7607924-. Full Text

Lim, S, McKee, JL, Woloszyn, L, Amit, Y, Freedman, DJ, Sheinberg, DL, and Brunel, N. "Inferring learning rules from distributions of firing rates in cortical neurons." Nature neuroscience 18.12 (December 2015): 1804-1810. Full Text Open Access Copy

Alemi, A, Baldassi, C, Brunel, N, and Zecchina, R. "A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks." PLoS Computational Biology 11.8 (August 20, 2015): e1004439-. Full Text Open Access Copy