Professor of Neurobiology
Professor of Physics (Joint)
Member of the Center for Cognitive Neuroscience
Faculty Network Member of the Duke Institute for Brain Sciences
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.
Brunel, N., and X. J. Wang. “Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition.” J Comput Neurosci, vol. 11, no. 1, July 2001, pp. 63–85. Pubmed, doi:10.1023/a:1011204814320. Full Text
Brunel, N., et al. “Effects of synaptic noise and filtering on the frequency response of spiking neurons.” Phys Rev Lett, vol. 86, no. 10, Mar. 2001, pp. 2186–89. Pubmed, doi:10.1103/PhysRevLett.86.2186. Full Text
Brunel, N. “Persistent activity and the single-cell frequency-current curve in a cortical network model.” Network, vol. 11, no. 4, Nov. 2000, pp. 261–80.
Compte, A., et al. “Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model.” Cereb Cortex, vol. 10, no. 9, Sept. 2000, pp. 910–23. Pubmed, doi:10.1093/cercor/10.9.910. Full Text
Brunel, N. “Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.” J Comput Neurosci, vol. 8, no. 3, May 2000, pp. 183–208. Pubmed, doi:10.1023/a:1008925309027. Full Text
Brunel, N., and V. Hakim. “Fast global oscillations in networks of integrate-and-fire neurons with low firing rates.” Neural Comput, vol. 11, no. 7, Oct. 1999, pp. 1621–71. Pubmed, doi:10.1162/089976699300016179. Full Text
Brunel, N., and S. Sergi. “Firing frequency of leaky intergrate-and-fire neurons with synaptic current dynamics.” J Theor Biol, vol. 195, no. 1, Nov. 1998, pp. 87–95. Pubmed, doi:10.1006/jtbi.1998.0782. Full Text
Brunel, N., and J. P. Nadal. “Mutual information, Fisher information, and population coding.” Neural Comput, vol. 10, no. 7, Oct. 1998, pp. 1731–57. Pubmed, doi:10.1162/089976698300017115. Full Text
Nadal, J. P., et al. “Nonlinear feedforward networks with stochastic outputs: infomax implies redundancy reduction.” Network, vol. 9, no. 2, May 1998, pp. 207–17.
Brunel, N., et al. “Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network.” Network, vol. 9, no. 1, Feb. 1998, pp. 123–52.