Nicolas Brunel

Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence

This study develops computational models showing that adolescent brain changes like pruning and myelination create strong attractors for emotionally salient memories, potentially contributing to psychiatric disorders.

A Spatially Structured Spiking Network Model of Beta Traveling Waves and Their Attenuation in Motor Cortex

This paper models beta traveling waves in primate motor cortex using a spiking network with spatial connectivity, showing they arise from Turing-Hopf instabilities and are modulated by external inputs.

Behavioral state and stimulus strength regulate the role of somatostatin interneurons in stabilizing network activity

The paper shows that somatostatin interneurons in mouse visual cortex play a greater role in stabilizing network activity with stronger sensory input and increased arousal.

Climbing fibers selectively recruit disinhibitory interneurons to enhance dendritic calcium signaling in cerebellar Purkinje cells

The paper investigates how climbing fiber inputs selectively excite disinhibitory interneurons to enhance dendritic calcium signaling in Purkinje cells, supporting cerebellar learning.

Impact of dendritic non-linearities on the computational capabilities of neurons

This paper shows that dendritic non-linearities improve a neuron's computational power by increasing learned associations, learning speed, and generalization performance.

Inter- and Intrahemispheric Sources of Vestibular Signals to V1

The paper identifies inter- and intrahemispheric sources of vestibular signals to V1, showing that the ipsilateral pulvinar and contralateral visual cortex provide head movement signals with specific directional biases.

Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules

The paper shows that varying external inputs to a network with heterogeneous plasticity rules can control retrieval speed and generate distinct preparatory and execution activity patterns.

Inhibitory Plasticity Enhances Sequence Storage Capacity and Retrieval Robustness

This study shows inhibitory plasticity improves sequence storage capacity and retrieval robustness in balanced E-I networks through simulations and dynamical mean-field theory.

Attractor neural networks with double well synapses

This paper introduces a neural network model with double-well synapses, showing it has a power law storage capacity scaling and improved robustness to noise compared to single-well synapse models.

Visual image familiarity learning at multiple timescales in the primate inferotemporal cortex

The paper investigates how neural activity in the primate inferotemporal cortex changes with visual familiarity over multiple timescales, finding that both slow and fast plasticity rules contribute to learning.

Cortico-thalamic communication for action coordination in a skilled motor sequence

The paper identifies a corticothalamic channel in the motor cortex that coordinates skilled motor sequences by amplifying thalamic integration of sensorimotor inputs.

Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys

The paper investigates how optogenetic stimulation reshuffles firing rates in mouse and monkey V1 by analyzing neuronal responses and developing theoretical models.

Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex

This paper develops a recurrent neural network model of motor cortex to show that external inputs shape preparatory activity while recurrent connectivity dominates during movement execution.

Non-monotonic effects of GABAergic synaptic inputs on neuronal firing

This paper investigates how GABAergic synaptic inputs can have non-monotonic effects on neuronal firing, showing both excitatory and inhibitory behaviors depending on the reversal potential and input strength.

Emergence of irregular activity in networks of strongly coupled conductance-based neurons

The paper shows that networks of strongly coupled conductance-based neurons exhibit asynchronous irregular activity and broad firing rate distributions when synaptic efficacy is of order 1/log(K), maintained by a drift-diffusion balance rather than current fluctuations.

Multiple forms of working memory emerge from synapse-astrocyte interactions

This paper proposes that astrocyte-mediated modulations of synaptic transmitter release can give rise to different forms of working memory through network activity and synaptic-astrocyte geometry.

Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning

The paper investigates how temporally asymmetric Hebbian learning in neural networks generates sequential activity that matches experimental observations.

Response nonlinearities in networks of spiking neurons

The paper investigates nonlinear responses in spiking neuron networks, showing nonlinearities exist at finite coupling and can be captured by rate models.

Inhibition stabilization is a widespread property of cortical networks

The paper tests inhibition-stabilized network (ISN) models in mouse cortex and finds evidence for ISN operation across multiple areas and states.

Altered spike timing-dependent plasticity rules in physiological calcium

The paper investigates STDP under physiological Ca2+ concentrations and finds that plasticity rules change significantly, with specific activity regimes restoring classical STDP profiles.

Acetylcholine modulates cerebellar granule cell spiking by regulating the balance of synaptic excitation and inhibition

This study shows acetylcholine modulates cerebellar granule cell spiking by altering the balance of excitation and inhibition through effects on Golgi cells and mossy fibers.

Unsupervised learning of persistent and sequential activity

This paper investigates how a neural network with plastic synapses can learn persistent and sequential activity through unsupervised learning rules based on input statistics.

Cerebellar learning using perturbations

This paper proposes a computational framework for cerebellar learning using perturbations and validates it with in vitro experiments, contradicting the current consensus on synaptic plasticity.

Attractor dynamics in networks with learning rules inferred from in vivo data

The paper infers learning rules and pattern distributions from in vivo data to model attractor dynamics in inferior temporal cortex, revealing optimized storage of attractor states.