Nicolas Brunel

Nicolas Brunel

Professor of Neurobiology

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

Overview

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.

Education & Training

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

Selected Grants

Large-scale, neuronal ensemble recordings in motor cortex of the behaving marmoset awarded by University of Chicago (Principal Investigator). 2018 to 2019

Pages

Dubreuil, Alexis M., and Nicolas Brunel. “Storing structured sparse memories in a multi-modular cortical network model..” J Comput Neurosci, vol. 40, no. 2, Apr. 2016, pp. 157–75. Pubmed, doi:10.1007/s10827-016-0590-z. Full Text

De Pittà, Maurizio, and Nicolas Brunel. “Modulation of Synaptic Plasticity by Glutamatergic Gliotransmission: A Modeling Study..” Neural Plast, vol. 2016, 2016. Pubmed, doi:10.1155/2016/7607924. Full Text

Lim, Sukbin, et al. “Inferring learning rules from distributions of firing rates in cortical neurons..” Nat Neurosci, vol. 18, no. 12, Dec. 2015, pp. 1804–10. Pubmed, doi:10.1038/nn.4158. Full Text Open Access Copy

Alemi, Alireza, et al. “A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks..” Plos Comput Biol, vol. 11, no. 8, Aug. 2015. Pubmed, doi:10.1371/journal.pcbi.1004439. Full Text Open Access Copy

Ostojic, Srdjan, et al. “Neuronal morphology generates high-frequency firing resonance..” J Neurosci, vol. 35, no. 18, May 2015, pp. 7056–68. Pubmed, doi:10.1523/JNEUROSCI.3924-14.2015. Full Text

Tartaglia, Elisa M., et al. “Modulation of network excitability by persistent activity: how working memory affects the response to incoming stimuli..” Plos Comput Biol, vol. 11, no. 2, Feb. 2015. Pubmed, doi:10.1371/journal.pcbi.1004059. Full Text Open Access Copy

Barbieri, Francesca, et al. “Stimulus dependence of local field potential spectra: experiment versus theory..” J Neurosci, vol. 34, no. 44, Oct. 2014, pp. 14589–605. Pubmed, doi:10.1523/JNEUROSCI.5365-13.2014. Full Text

Higgins, David, et al. “Memory maintenance in synapses with calcium-based plasticity in the presence of background activity..” Plos Comput Biol, vol. 10, no. 10, Oct. 2014. Pubmed, doi:10.1371/journal.pcbi.1003834. Full Text Open Access Copy

Dubreuil, Alexis M., et al. “Memory capacity of networks with stochastic binary synapses..” Plos Comput Biol, vol. 10, no. 8, Aug. 2014. Pubmed, doi:10.1371/journal.pcbi.1003727. Full Text Open Access Copy

Clopath, Claudia, et al. “A cerebellar learning model of vestibulo-ocular reflex adaptation in wild-type and mutant mice..” J Neurosci, vol. 34, no. 21, May 2014, pp. 7203–15. Pubmed, doi:10.1523/JNEUROSCI.2791-13.2014. Full Text

Pages

Brunel, N. “Quantitative modeling of local Hebbian reverberations in primate cortex.” International Journal of Neural Systems, Supplementary Issue, 1995, edited by D. J. Amit et al., WORLD SCIENTIFIC PUBL CO PTE LTD, 1995, pp. 13–17.

Pages