Brunel Lab

Nicolas Brunel, PhD, PI

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.

Our research has spanned five major directions:

  1. Dynamics of networks of spiking neurons: What are the mechanisms of the low-rate, highly irregular activity that is ubiquitous in cerebral cortex? To answer this question, we have introduced models of networks of integrate-and-fire neurons composed of two populations of neurons (excitatory and inhibitory) with random connectivity, and systematically analyzed their dynamics.
  2. Stochastic dynamics of single spiking neurons: To understand better the factors that shape the neuronal response to transient inputs, we have investigated mathematically the dynamics of the instantaneous firing probability in single spiking neuron models of increasing complexity in the presence of noisy inputs. We have characterized extensively how the instantaneous firing rate depends on the statistics of the inputs, in both static (f-I curve) and dynamic (response to transient inputs) conditions.
  3. Mechanisms of persistent activity in cortical circuits: Selective persistent activity has been hypothesized to be the substrate of working memory in cortical circuits during delayed response tasks in behaving primates. We have built network models of spiking neurons that are able to reproduce the phenomenology of these experiments.
  4. Synaptic plasticity rules: What are the rules of synaptic plasticity? We have followed three complementary approaches to make progress on this question: (i) building a synaptic plasticity model from in vitro data (ii) inferring plasticity rules from in vivo data (iii) deriving learning rules from optimality principles.
  5. Statistics of networks optimizing information storage: What are the consequences of optimizing the amount of stored information, or its robustness, on the structure and statistics of neuronal connectivity? We have computed analytically the statistics of synaptic connectivity in a network that maximizes the amount of stored information, with a given robustness constraint so the information can be retrieved robustly in the presence of noise, and shown than the resulting statistics are in good agreement with in vitro cortical data.

Selected publications

N Brunel (2016), Is cortical connectivity optimized for storing information?, Nature Neurosci., 19:749-755

S Lim, J McKee, L Woloszyn, Y Amit, D Freedman, D Sheinberg and N Brunel (2015), Inferring learning rules from distributions of firing rates in cortical neurons, Nature Neurosci., 18:1804-1810

M Graupner and N Brunel (2012), A calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate and dendritic location, Proc Natl Acad Sci U S A., 109:3991-3996

N Brunel, V Hakim, P Isope, JP Nadal and B Barbour (2004), Optimal information storage and the distribution of synaptic weights: Perceptron vs. Purkinje cell, Neuron, 43:745-757

N Fourcaud-Trocme, D Hansel, C van Vreeswijk and N Brunel (2003), How spike generation mechanisms determine the neuronal response to fluctuating inputs, J. Neurosci., 23:11628-11640

N Brunel, F Chance, N Fourcaud and L Abbott (2001), Effects of synaptic noise and filtering on the frequency response of spiking neurons, Phys. Rev. Lett., 86:2186-2189

N Brunel (2000), Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons, J. Comp. Neurosci 8:183-208

N Brunel and V Hakim (1999), Fast global oscillations in networks of integrate-and-fire neurons with low firing rates, Neural Comp., 11:1621-1671

DJ Amit and N Brunel (1997), Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex, Cereb.~Cortex, 7:237-252

Recent Posts

Friday, December 18, 2020 - 10:00
Maxwell Gillett

Neurobiology PhD candidate Maxwell Gillett (Brunel Lab) will defend his dissertation Learning and Retrieval of Sequential Activity Using Temp