Nicolas Brunel

Nicolas Brunel

Professor of Neurobiology

Professor of Physics (Joint)

Member of the Center for Cognitive Neuroscience

Faculty Network Member of the Duke Institute for Brain Sciences

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

Amit, Daniel, and Nicolas Brunel. “Dynamics of a recurrent network of spiking neurons before and following learning.” Network: Computation in Neural Systems, vol. 8, no. 4, Jan. 1997, pp. 373–404. Manual, doi:10.1088/0954-898X_8_4_003. Full Text

Brunel, N. “Hebbian learning of context in recurrent neural networks..” Neural Comput, vol. 8, no. 8, Nov. 1996, pp. 1677–710. Pubmed, doi:10.1162/neco.1996.8.8.1677. Full Text

Brunel, Nicolas, and Riccardo Zecchina. “A SIMPLE GEOMETRICAL BOUND FOR REPLICA SYMMETRY STABILITY IN NEURAL NETWORKS MODELS.” Modern Physics Letters B, vol. 09, no. 18, World Scientific Pub Co Pte Lt, Aug. 1995, pp. 1159–64. Manual, doi:10.1142/s0217984995001157. Full Text

Amit, Daniel, and Nicolas Brunel. “Learning internal representations in an attractor neural network with analogue neurons.” Network: Computation in Neural Systems, vol. 6, no. 3, Informa UK Limited, Aug. 1995, pp. 359–88. Manual, doi:10.1088/0954-898x/6/3/004. Full Text

Brunel, Nicolas. “Storage capacity of neural networks: Effect of the fluctuations of the number of active neurons per memory.” Journal of Physics A: Mathematical and General, vol. 27, no. 14, Dec. 1994, pp. 4783–89. Manual, doi:10.1088/0305-4470/27/14/009. Full Text

Brunel, Nicolas. “Dynamics of an attractor neural network converting temporal into spatial correlations.” Network: Computation in Neural Systems, vol. 5, no. 4, Informa UK Limited, Nov. 1994, pp. 449–70. Crossref, doi:10.1088/0954-898x/5/4/003. Full Text

Amit, D. J., et al. “Correlations of cortical Hebbian reverberations: theory versus experiment..” J Neurosci, vol. 14, no. 11 Pt 1, Nov. 1994, pp. 6435–45.

Brunel, N., and R. Zecchina. “Response functions improving performance in analog attractor neural networks..” Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics, vol. 49, no. 3, Mar. 1994, pp. R1823–26. Pubmed, doi:10.1103/physreve.49.r1823. Full Text

Brunel, N. “Effect of synapse dilution on the memory retrieval in structured attractor neural networks.” Journal De Physique I, vol. 3, no. 8, EDP Sciences, Aug. 1993, pp. 1693–715. Crossref, doi:10.1051/jp1:1993210. Full Text

Amit, D. J., and N. Brunel. “Adequate input for learning in attractor neural networks.” Network: Computation in Neural Systems, vol. 4, no. 2, Jan. 1993, pp. 177–94. Scopus, doi:10.1088/0954-898X_4_2_003. Full Text

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