Nicolas Brunel

Nicolas Brunel

Professor of Neurobiology

Professor of Physics (Joint)

Member of the Center for Cognitive Neuroscience

Faculty Network Member of the Duke Institute for Brain Sciences

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

Brunel, Nicolas, and Mark C. W. van Rossum. “Lapicque's 1907 paper: from frogs to integrate-and-fire.Biol Cybern, vol. 97, no. 5–6, Dec. 2007, pp. 337–39. Pubmed, doi:10.1007/s00422-007-0190-0. Full Text

Graupner, Michael, and Nicolas Brunel. “STDP in a bistable synapse model based on CaMKII and associated signaling pathways.Plos Comput Biol, vol. 3, no. 11, Nov. 2007, p. e221. Pubmed, doi:10.1371/journal.pcbi.0030221. Full Text

Baldassi, Carlo, et al. “Efficient supervised learning in networks with binary synapses.Proc Natl Acad Sci U S A, vol. 104, no. 26, June 2007, pp. 11079–84. Pubmed, doi:10.1073/pnas.0700324104. Full Text

Barbieri, Francesca, and Nicolas Brunel. “Irregular persistent activity induced by synaptic excitatory feedback.Front Comput Neurosci, vol. 1, 2007, p. 5. Pubmed, doi:10.3389/neuro.10.005.2007. Full Text

Baldassi, Carlo, et al. “Efficient supervised learning in networks with binary synapses.Proc. Natl. Acad. Sci. Usa, vol. 104, 2007, pp. 11079–84.

Brunel, Nicolas, and David Hansel. “How noise affects the synchronization properties of recurrent networks of inhibitory neurons.Neural Comput, vol. 18, no. 5, May 2006, pp. 1066–110. Pubmed, doi:10.1162/089976606776241048. Full Text

Geisler, Caroline, et al. “Contributions of intrinsic membrane dynamics to fast network oscillations with irregular neuronal discharges.J Neurophysiol, vol. 94, no. 6, Dec. 2005, pp. 4344–61. Pubmed, doi:10.1152/jn.00510.2004. Full Text

Roxin, Alex, et al. “Role of delays in shaping spatiotemporal dynamics of neuronal activity in large networks.Phys Rev Lett, vol. 94, no. 23, June 2005, p. 238103. Pubmed, doi:10.1103/PhysRevLett.94.238103. Full Text

Boucheny, Christian, et al. “A continuous attractor network model without recurrent excitation: maintenance and integration in the head direction cell system.J Comput Neurosci, vol. 18, no. 2, Mar. 2005, pp. 205–27. Pubmed, doi:10.1007/s10827-005-6559-y. Full Text

Brunel, N. Course 10 Network models of memory. Vol. 80, no. C, Jan. 2005, pp. 407–76. Scopus, doi:10.1016/S0924-8099(05)80016-2. Full Text