Nicolas Brunel, Adjunct Professor of Neurobiology  

Nicolas Brunel

Office Location: 311 Research Drive, Durham, NC 27710
Email Address: nicolas.brunel@duke.edu
Web Page: http://webhome.phy.duke.edu/~nb170

Specialties:
Biological physics
Nonlinear dynamics and complex systems

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

Recent Publications   (More Publications)

  1. Bouvier, G; Sanzeni, A; Hamada, E; Brunel, N; Scanziani, M, Inter- and Intrahemispheric Sources of Vestibular Signals to V1., bioRxiv (December, 2024) [doi]  [abs].
  2. Li, Y; An, X; Mulcahey, PJ; Qian, Y; Xu, XH; Zhao, S; Mohan, H; Suryanarayana, SM; Bachschmid-Romano, L; Brunel, N; Whishaw, IQ; Huang, ZJ, Cortico-thalamic communication for action coordination in a skilled motor sequence. (October, Preprint, 2024) [doi] .
  3. Cammarata, CM; Pei, Y; Shields, BC; Lim, SSX; Hawley, T; Li, JY; St Amand, D; Brunel, N; Tadross, MR; Glickfeld, LL, Behavioral state and stimulus strength regulate the role of somatostatin interneurons in stabilizing network activity., bioRxiv (September, 2024) [doi]  [abs].
  4. Gillett, M; Brunel, N, Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules., Elife, vol. 12 (August, 2024) [doi]  [abs].
  5. Lauditi, C; Malatesta, EM; Pittorino, F; Baldassi, C; Brunel, N; Zecchina, R, Impact of dendritic non-linearities on the computational capabilities of neurons (July, Preprint, 2024) .

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.