Nicolas Brunel
Professor of Neurobiology
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.
Selected Grants
Medical Scientist Training Program awarded by National Institutes of Health (Preceptor). 2022 to 2027
Neurobiology Training Program awarded by National Institutes of Health (Mentor). 2019 to 2024
Canonical computations for motor learning by the cerebellar cortex micro-circuit awarded by National Institutes of Health (Co-Principal Investigator). 2019 to 2024
Striatal Microcircuit Drivers of Adaptive Learning in Habit Formation awarded by National Institutes of Health (Co Investigator). 2018 to 2023
Large-scale, neuronal ensemble recordings in motor cortex of the behaving marmoset awarded by University of Chicago (Principal Investigator). 2018 to 2022
Investigating ripple oscillations as a mechanism for human memory retrieval awarded by National Institutes of Health (Principal Investigator). 2019 to 2022
Medical Scientist Training Program awarded by National Institutes of Health (Mentor). 1997 to 2022
CRCNS: Multiscale dynamics of cortical circuits for visual recognition & memory awarded by University of Chicago (Principal Investigator). 2017 to 2022
An Integrated Nonparametric Bayesian and Deep Neural Network Framework for Biologically-Inspired Lifelong Learning awarded by Defense Advanced Research Projects Agency (Co Investigator). 2018 to 2022
Circuitry underlying response summation in mouse and primate: theory and experiment awarded by Salk Institute Biotechnology/Industrial Associatio (Principal Investigator). 2018 to 2021
Pages
Brunel, Nicolas, and Vincent Hakim. “Fokker-Planck Equation.” Encyclopedia of Computational Neuroscience, Springer New York, 2015, pp. 1222–26. Crossref, doi:10.1007/978-1-4614-6675-8_60. Full Text
Brunel, Nicolas, and Vincent Hakim. “Population Density Model.” Encyclopedia of Computational Neuroscience, Springer New York, 2015, pp. 2447–65. Crossref, doi:10.1007/978-1-4614-6675-8_74. Full Text
Brunel, Nicolas, and Vincent Hakim. “Fokker-Planck Equation.” Encyclopedia of Computational Neuroscience, Springer New York, 2014, pp. 1–5. Crossref, doi:10.1007/978-1-4614-7320-6_60-2. Full Text
Brunel, Nicolas, and Vincent Hakim. “Population Density Models.” Encyclopedia of Computational Neuroscience, Springer New York, 2013, pp. 1–24. Crossref, doi:10.1007/978-1-4614-7320-6_74-1. Full Text
Brunel, Nicolas, and Vincent Hakim. “Fokker-Planck Equation.” Encyclopedia of Computational Neuroscience, Springer New York, 2013, pp. 1–6. Crossref, doi:10.1007/978-1-4614-7320-6_60-1. Full Text
Brunel, Nicolas. “Dynamics of neural networks.” Principles of Neural Coding, 2013, pp. 489–512. Manual, doi:10.1201/b14756. Full Text
Brunel, Nicolas, and Vincent Hakim. “Neuronal Dynamics.” Encyclopedia of Complexity and Systems Science, Springer New York, 2009, pp. 6099–116. Crossref, doi:10.1007/978-0-387-30440-3_359. Full Text
Abed Zadeh, Aghil, et al. “Non-monotonic effects of GABAergic synaptic inputs on neuronal firing.” Plos Comput Biol, vol. 18, no. 6, June 2022, p. e1010226. Pubmed, doi:10.1371/journal.pcbi.1010226. Full Text
Sanzeni, A., et al. “Emergence of Irregular Activity in Networks of Strongly Coupled Conductance-Based Neurons.” Physical Review X, vol. 12, no. 1, Mar. 2022. Scopus, doi:10.1103/PhysRevX.12.011044. Full Text
Feng, Yu, and Nicolas Brunel. Storage capacity of networks with discrete synapses and sparsely encoded memories. Dec. 2021. Open Access Copy
Pereira-Obilinovic, Ulises, et al. Forgetting leads to chaos in attractor networks. Nov. 2021. Open Access Copy
Aljadeff, Johnatan, et al. “From synapse to network: models of information storage and retrieval in neural circuits.” Curr Opin Neurobiol, vol. 70, Oct. 2021, pp. 24–33. Pubmed, doi:10.1016/j.conb.2021.05.005. Full Text Open Access Copy
Goldt, Sebastian, et al. Bayesian reconstruction of memories stored in neural networks from their connectivity. May 2021. Open Access Copy
Inglebert, Yanis, et al. “Synaptic plasticity rules with physiological calcium levels.” Proc Natl Acad Sci U S A, vol. 117, no. 52, Dec. 2020, pp. 33639–48. Pubmed, doi:10.1073/pnas.2013663117. Full Text Open Access Copy
Gillett, Maxwell, et al. “Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.” Proc Natl Acad Sci U S A, vol. 117, no. 47, Nov. 2020, pp. 29948–58. Pubmed, doi:10.1073/pnas.1918674117. Full Text Open Access Copy
Sanzeni, Alessandro, et al. Emergence of irregular activity in networks of strongly coupled conductance-based neurons. Sept. 2020. Open Access Copy
Sanzeni, Alessandro, et al. “Response nonlinearities in networks of spiking neurons.” Plos Comput Biol, vol. 16, no. 9, Sept. 2020, p. e1008165. Pubmed, doi:10.1371/journal.pcbi.1008165. Full Text Open Access Copy
Pages
Roxin, A., et al. “Rate models with delays and the dynamics of large networks of spiking neurons.” Progress of Theoretical Physics Supplement, vol. 161, 2006, pp. 68–85. Scopus, doi:10.1143/PTPS.161.68. Full Text
Fourcaud-Trocmé, Nicolas, and Nicolas Brunel. “Dynamics of the instantaneous firing rate in response to changes in input statistics.” J Comput Neurosci, vol. 18, no. 3, 2005, pp. 311–21. Pubmed, doi:10.1007/s10827-005-0337-8. Full Text
Brunel, N. “Dynamics of networks of randomly connected excitatory and inhibitory spiking neurons.” J Physiol Paris, vol. 94, no. 5–6, 2000, pp. 445–63. Pubmed, doi:10.1016/s0928-4257(00)01084-6. Full Text
Brunel, N. “Phase diagrams of sparsely connected networks of excitatory and inhibitory spiking neurons.” Neurocomputing, vol. 32–33, 2000, pp. 307–12. Scopus, doi:10.1016/S0925-2312(00)00179-X. Full Text
Brunel, N., and X. J. Wang. “Fast network oscillations with intermittent principal cell firing in a model of a recurrent excitatory-inhibitory circuit.” European Journal of Neuroscience, vol. 12, BLACKWELL SCIENCE LTD, 2000, pp. 79–79.
Brunel, N., and J. P. Nadal. “Modeling memory: what do we learn from attractor neural networks?” C R Acad Sci Iii, vol. 321, no. 2–3, 1998, pp. 249–52. Pubmed, doi:10.1016/s0764-4469(97)89830-7. Full Text
Brunel, N. “Cross-correlations in sparsely connected recurrent networks of spiking neurons.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1327, 1997, pp. 31–36. Scopus, doi:10.1007/bfb0020128. Full Text
Brunel, Nicolas, and Jean-Pierre Nadal. “Optimal tuning curves for neurons spiking as a Poisson process.” Esann, edited by Michel Verleysen, D-Facto public, 1997.
Ninio, J., and N. Brunel. “Time to detect a single difference between two correlated images.” Perception, vol. 25, PION LTD, 1996, pp. 89–89.
Brunel, N., and D. J. Amit. “Learning internal representations in an analog attractor neural network.” International Journal of Neural Systems, Supplementary Issue, 1995, edited by D. J. Amit et al., WORLD SCIENTIFIC PUBL CO PTE LTD, 1995, pp. 19–23.