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

Ostojic, Srdjan, and Nicolas Brunel. “From spiking neuron models to linear-nonlinear models.Plos Comput Biol, vol. 7, no. 1, Jan. 2011, p. e1001056. Pubmed, doi:10.1371/journal.pcbi.1001056. Full Text Open Access Copy

Mazzoni, Alberto, et al. “Cortical dynamics during naturalistic sensory stimulations: experiments and models.J Physiol Paris, vol. 105, no. 1–3, Jan. 2011, pp. 2–15. Pubmed, doi:10.1016/j.jphysparis.2011.07.014. Full Text

Hamaguchi, Kosuke, et al. “Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.J Neurophysiol, vol. 105, no. 1, Jan. 2011, pp. 487–500. Pubmed, doi:10.1152/jn.00858.2009. Full Text

Ledoux, Erwan, and Nicolas Brunel. “Dynamics of networks of excitatory and inhibitory neurons in response to time-dependent inputs.Front Comput Neurosci, vol. 5, 2011, p. 25. Pubmed, doi:10.3389/fncom.2011.00025. Full Text Open Access Copy

Mazzoni, Alberto, et al. “Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model.Neuroimage, vol. 52, no. 3, Sept. 2010, pp. 956–72. Pubmed, doi:10.1016/j.neuroimage.2009.12.040. Full Text

Panzeri, Stefano, et al. “Sensory neural codes using multiplexed temporal scales.Trends Neurosci, vol. 33, no. 3, Mar. 2010, pp. 111–20. Pubmed, doi:10.1016/j.tins.2009.12.001. Full Text

Graupner, Michael, and Nicolas Brunel. “Mechanisms of induction and maintenance of spike-timing dependent plasticity in biophysical synapse models.Front Comput Neurosci, vol. 4, 2010. Pubmed, doi:10.3389/fncom.2010.00136. Full Text

Brunel, Nicolas, and Frédéric Lavigne. “Semantic priming in a cortical network model.J Cogn Neurosci, vol. 21, no. 12, Dec. 2009, pp. 2300–19. Pubmed, doi:10.1162/jocn.2008.21156. Full Text

Graupner, Michael, and Nicolas Brunel. “A bitable synaptic model with transitions between states induced by calcium dynamics: theory vs experiment.” Bmc Neuroscience, vol. 10, no. S1, Springer Science and Business Media LLC, Sept. 2009. Crossref, doi:10.1186/1471-2202-10-s1-o15. Full Text

Ostojic, Srdjan, et al. “How connectivity, background activity, and synaptic properties shape the cross-correlation between spike trains.J Neurosci, vol. 29, no. 33, Aug. 2009, pp. 10234–53. Pubmed, doi:10.1523/JNEUROSCI.1275-09.2009. Full Text