Nicolas Brunel, Duke School of Medicine Distinguished Professor in Neuroscience  

Nicolas Brunel

Office Location: 311 Research Drive, Durham, NC 27710
Office Phone: +1 919 684 8684
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. Feng, Y; Brunel, N, Attractor neural networks with double well synapses., PLoS Comput Biol, vol. 20 no. 2 (February, 2024), pp. e1011354 [doi]  [abs].
  2. Sanzeni, A; Palmigiano, A; Nguyen, TH; Luo, J; Nassi, JJ; Reynolds, JH; Histed, MH; Miller, KD; Brunel, N, Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys., Neuron, vol. 111 no. 24 (December, 2023), pp. 4102-4115.e9 [2022.07.13.499597v1], [doi]  [abs].
  3. Li, Y; An, X; Qian, Y; Xu, XH; Zhao, S; Mohan, H; Bachschmid-Romano, L; Brunel, N; Whishaw, IQ; Huang, ZJ, Cortical network and projection neuron types that articulate serial order in a skilled motor behavior. (October, Preprint, 2023) [doi] .
  4. Bachschmid-Romano, L; Hatsopoulos, NG; Brunel, N, Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex., Elife, vol. 12 (May, 2023) [2022.02.19.481140v1], [doi]  [abs].
  5. Goldt, S; Krzakala, F; Zdeborová, L; Brunel, N, Bayesian reconstruction of memories stored in neural networks from their connectivity., PLoS Comput Biol, vol. 19 no. 1 (January, 2023), pp. e1010813 [2105.07416], [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.