Professor of Neurobiology
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.
Learning spatio-temporal statistics from the environment in recurrent networks awarded by Office of Naval Research (Principal Investigator). 2017 to 2019
CRCNS: US-French Research Proposal: Synaptic plasticity rules under physiological conditions for hippocampus and cerebellum awarded by National Science Foundation (Principal Investigator). 2017
Tartaglia, EM, and Brunel, N. "Bistability and up/down state alternations in inhibition-dominated randomly connected networks of LIF neurons." Scientific reports 7.1 (September 20, 2017): 11916-. Full Text
Zampini, V, Liu, JK, Diana, MA, Maldonado, PP, Brunel, N, and Dieudonné, S. "Mechanisms and functional roles of glutamatergic synapse diversity in a cerebellar circuit." eLife 5 (September 19, 2016). Full Text Open Access Copy
Brunel, N. "Is cortical connectivity optimized for storing information?." Nature neuroscience 19.5 (May 2016): 749-755. Full Text
Dubreuil, AM, and Brunel, N. "Storing structured sparse memories in a multi-modular cortical network model." Journal of computational neuroscience 40.2 (April 2016): 157-175. Full Text
Bouvier, G, Higgins, D, Spolidoro, M, Carrel, D, Mathieu, B, Léna, C, Dieudonné, S, Barbour, B, Brunel, N, and Casado, M. "Burst-Dependent Bidirectional Plasticity in the Cerebellum Is Driven by Presynaptic NMDA Receptors." Cell reports 15.1 (April 2016): 104-116. Full Text
De Pittà, M, and Brunel, N. "Modulation of Synaptic Plasticity by Glutamatergic Gliotransmission: A Modeling Study." Neural plasticity 2016 (January 2016): 7607924-. Full Text
Lim, S, McKee, JL, Woloszyn, L, Amit, Y, Freedman, DJ, Sheinberg, DL, and Brunel, N. "Inferring learning rules from distributions of firing rates in cortical neurons." Nature neuroscience 18.12 (December 2015): 1804-1810. Full Text Open Access Copy
Alemi, A, Baldassi, C, Brunel, N, and Zecchina, R. "A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks." PLoS Computational Biology 11.8 (August 20, 2015): e1004439-. Full Text Open Access Copy
Roxin, A, Brunel, N, and Hansel, D. "Rate Models with Delays and the Dynamics of Large Networks of Spiking Neurons." 2006. Full Text
Fourcaud-Trocmé, N, and Brunel, N. "Dynamics of the instantaneous firing rate in response to changes in input statistics." June 2005. Full Text
Brunel, N. "Dynamics of networks of randomly connected excitatory and inhibitory spiking neurons." September 2000. Full Text
Brunel, N, and Wang, XJ. "Fast network oscillations with intermittent principal cell firing in a model of a recurrent excitatory-inhibitory circuit." 2000.
Brunel, N, and Nadal, JP. "Modeling memory: what do we learn from attractor neural networks?." February 1998. Full Text
Brunel, N. "Cross-correlations in sparsely connected recurrent networks of spiking neurons." January 1, 1997.
Brunel, N, and Nadal, J-P. "Optimal tuning curves for neurons spiking as a Poisson process." D-Facto public, 1997.
Ninio, J, and Brunel, N. "Time to detect a single difference between two correlated images." 1996.
Brunel, N, and Amit, DJ. "Learning internal representations in an analog attractor neural network." 1995.