Nicolas Brunel, Professor of Neurobiology and Physics and Member of Center for Cognitive Neuroscience and Faculty Network Member of Duke Institute for Brain Sciences  

Nicolas Brunel

Office Location: 311 Research Drive, Durham, NC 27710
Office Phone: (919) 684-8684
Email Address:
Web Page:

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

Recent Publications   (More Publications)

  1. Vaz, AP; Inati, SK; Brunel, N; Zaghloul, KA, Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory., Science, vol. 363 no. 6430 (March, 2019), pp. 975-978, American Association for the Advancement of Science (AAAS) [doi]  [abs].
  2. Bouvier, G; Aljadeff, J; Clopath, C; Bimbard, C; Ranft, J; Blot, A; Nadal, J-P; Brunel, N; Hakim, V; Barbour, B, Cerebellar learning using perturbations., Elife, vol. 7 (November, 2018) [doi]  [abs].
  3. Pereira, U; Brunel, N, Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data., Neuron, vol. 99 no. 1 (July, 2018), pp. 227-238.e4 [doi]  [abs].
  4. Martí, D; Brunel, N; Ostojic, S, Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks., Phys Rev E, vol. 97 no. 6-1 (June, 2018), pp. 062314 [doi]  [abs].
  5. Tartaglia, EM; Brunel, N, Bistability and up/down state alternations in inhibition-dominated randomly connected networks of LIF neurons., Scientific Reports, vol. 7 no. 1 (September, 2017), pp. 11916 [doi]  [abs].

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.