Nicolas Brunel, Professor of Neurobiology and Physics  

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

Recent Publications   (More Publications)

  1. 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].
  2. 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].
  3. Titley, HK; Brunel, N; Hansel, C, Toward a Neurocentric View of Learning., Neuron, vol. 95 no. 1 (July, 2017), pp. 19-32 [doi]  [abs].
  4. Zampini, V; Liu, JK; Diana, MA; Maldonado, PP; Brunel, N; DieudonnĂ©, S, Mechanisms and functional roles of glutamatergic synapse diversity in a cerebellar circuit., Elife, vol. 5 (September, 2016) [doi]  [abs].
  5. Brunel, N, Is cortical connectivity optimized for storing information?, Nature Neuroscience, vol. 19 no. 5 (May, 2016), pp. 749-755 [doi]  [abs].

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.