Nicolas Brunel

Professor

Department of Neurobiology

The University of Chicago
947 E. 58th St., MC0926
Chicago, IL 60637 

Email: nbrunel@galton.uchicago.edu
Phone: (773) 702-9095
Office/Lab: AB320

Brunel Lab website

 

Research Summary

We use theoretical tools from applied mathematics and statistical physics to understand the dynamics of neural systems, and how they encode and store information. Our research effort have been focused on the single synaptic level, with the development of a new synaptic plasticity model that captures a large body of experimental data; and on the single neuron level, with the mathematical analysis of the stochastic dynamics of a large range of simplified spiking neuron models, and the development of a new spiking neuron model (the EIF model) that captures accurately spiking generation dynamics of real neurons. At the network level, we have developed tools for analyzing network states with irregular single neuron activity, and investigated the mechanisms of synchronized oscillations in randomly connected networks. We have studied information storage in large networks of neurons, and shown that an information optimization principle can explain many experimentally observed features of synaptic connectivity. Our work has been applied to understand phenomena such as persistent activity seen in delayed response experiments in behaving monkeys, as well as oscillations in various systems (monkey V1, rodent cerebellum).

 

Select Publications

Graupner M, Brunel N. Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location. Proc Natl Acad Sci U S A. 2012 Mar 6;109(10):3991-6.

Roxin A, Brunel N, Hansel D, Mongillo G, van Vreeswijk C. On the distribution of firing rates in networks of cortical neurons. J Neurosci. 2011 Nov 9;31(45):16217-26.

Ostojic S, Brunel N. From spiking neuron models to linear-nonlinear models. PLoS Comput Biol. 2011 Jan 20;7(1):e1001056.

Panzeri S, Brunel N, Logothetis NK, Kayser C. Sensory neural codes using multiplexed temporal scales. Trends Neurosci. 2010 Mar;33(3):111-20.

Dugué GP, Brunel N, Hakim V, Schwartz E, Chat M, Lévesque M, Courtemanche R, Léna C, Dieudonné S. Electrical coupling mediates tunable low-frequency oscillations and resonance in the cerebellar Golgi cell network. Neuron. 2009 Jan 15;61(1):126-39.

Mazzoni A, Panzeri S, Logothetis NK, Brunel N. Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons. PLoS Comput Biol. 2008 Dec;4(12):e1000239.

Roxin A, Hakim V, Brunel N. The statistics of repeating patterns of cortical activity can be reproduced by a model network of stochastic binary neurons. J Neurosci. 2008 Oct 15;28(42):10734-45.

de Solages C, Szapiro G, Brunel N, Hakim V, Isope P, Buisseret P, Rousseau C,  Barbour B, Léna C. High-frequency organization and synchrony of activity in the purkinje cell layer of the cerebellum. Neuron. 2008 Jun 12;58(5):775-88.

Barbour B, Brunel N, Hakim V, Nadal JP. What can we learn from synaptic weight distributions? Trends Neurosci. 2007 Dec;30(12):622-9.

Baldassi C, Braunstein A, Brunel N, Zecchina R. Efficient supervised learning in networks with binary synapses. Proc Natl Acad Sci U S A. 2007 Jun 26;104(26):11079-84.

Brunel N, Hakim V, Isope P, Nadal JP, Barbour B. Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell. Neuron. 2004 Sep 2;43(5):745-57.

Fourcaud-Trocmé N, Hansel D, van Vreeswijk C, Brunel N. How spike generation mechanisms determine the neuronal response to fluctuating inputs. J Neurosci. 2003 Dec 17;23(37):11628-40.