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: nicolas.brunel@duke.edu
Web Page: https://www.neuro.duke.edu/research/faculty-labs/brunel-lab

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

Teaching (Spring 2020):

  • Physics 567.01, Theoretical neuroscience Synopsis
    Physics 150, TuTh 08:30 AM-09:45 AM
  • Neurobio 735.01, Neurobiology quantitative Synopsis
    Brynresmed 301, TuTh 03:00 PM-04:30 PM

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. U Pereira and N Brunel, Unsupervised learning of persistent and sequential activity, Frontiers in Comp. Neurosci. (Accepted, 2019) (biorxiv 414813.) .
  3. A Sanzeni, B. Akitake, HC Goldbach, CE Leedy, N Brunel and M Histed, Inhibition stabilization is a widespread property of cortical networks (Preprint, 2019) (biorxiv 656710.) .
  4. TR Fore, N Taylor, N Brunel and C Hull, Acetylcholine modulates cerebellar granule cell spiking by regulating the balance of synaptic excitation and inhibition (Preprint, 2019) (biorxiv 760223.) .
  5. M Gillett, U Pereira and N Brunel, Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning (Preprint, 2019) (biorxiv 818773.) .

Highlight:
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.