Publications

(see also our Google Scholar profile)

Published

The dynamical regime of sensory cortex: stable dynamics around a single stimulus-tuned attractor account for patterns of noise variability

G Hennequin, Y Ahmadian*, D B Rubin*, M LengyelŦ, and KD MillerŦ
Neuron, 2018  

Neural networks subtract and conquer

G Hennequin
eLife, 2017  

Inhibitory plasticity: balance, control, and codependence

G Hennequin*, EJ Agnes*, and TP Vogels
Annu. Rev. Neurosci., 2017  

Optimal control of transient dynamics in balanced networks supports generation of complex movements

G Hennequin, TP VogelsŦ, and W GerstnerŦ
Neuron, 2014  

Fast sampling-based inference in balanced neuronal networks

G Hennequin, L Aitchison, and M Lengyel
NIPS, 2014  

Analog memories in a balanced rate-based network of E/I neurons

D Festa, G Hennequin, and M Lengyel
NIPS, 2014  

Synaptic plasticity in neural networks needs homeostasis with a fast rate detector

F Zenke, G Hennequin, and W Gerstner
PLoS Computational Biology, 2013  

Nonnormal amplification in random balanced neuronal networks

G Hennequin, TP Vogels, and W Gerstner
Phys. Rev. E, 2012  

STDP in adaptive neurons gives close-to-optimal information transmission

G Hennequin, W Gerstner, and JP Pfister
Frontiers in Computational Neuroscience, 2010  



Preprints

Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability

L Aitchison, G Hennequin, and M Lengyel
arXiv, 2018  

Asymptotic scaling properties of the posterior mean and variance in the Gaussian scale mixture model

R Echeveste, G Hennequin, and M Lengyel
arXiv, 2017  

Characterizing variability in nonlinear recurrent neuronal networks

G Hennequin, and M Lengyel
arXiv, 2016  



Conference abstracts

Flexible, optimal motor control in a thalamo-cortical circuit model

M Sadabadi, T-C Kao, and G Hennequin
COSYNE, 2018  

Orthogonal preparatory and movement subspaces in monkey, mouse, and model

T-C Kao, M Sadabadi, and G Hennequin
COSYNE, 2018  

Motor primitives in space and time via targeted gain modulation in cortical networks

J Stroud, M Porter, G Hennequin, and TP Vogels
COSYNE, 2018  

Probabilistic inference emerges from learning in neural circuits with a cost on reliability

L Aitchison, G Hennequin, and M Lengyel
COSYNE, 2018  

Hebb 'n' Dale: efficient coding by time-reversible dynamics in recurrent circuits

A Bernacchia, J Fiser, G Hennequin, and M Lengyel
COSYNE, 2018  

Limits on fast, high-dimensional information processing in recurrent circuits

V Rutten, and G Hennequin
COSYNE, 2017  

Cherchez les auxiliaires: interneurons are key for high-capacity attractor networks

D Festa, G Hennequin, and M Lengyel
COSYNE, 2017  

How much to gain: targeted gain modulation facilitates learning in recurrent motor circuits

J Stroud, G Hennequin, and TP Vogels
COSYNE, 2017  

Dale's principle preserves sequentiality in neural circuits

A Bernacchia, J Fiser, G Hennequin, and M Lengyel
COSYNE, 2017  

GSM=SSN: recurrent neural circuits optimised for probabilistic inference

R Echeveste, G Hennequin*, and M Lengyel*
COSYNE, 2017  

Balance out of control: robust stabilization of recurrent circuits via inhibitory plasticity

G Hennequin, and TP Vogels
COSYNE, 2016  

The dynamics of variability in nonlinear recurrent circuits

G Hennequin, and M Lengyel
COSYNE, 2014  

Fast sampling in recurrent neural circuits

G Hennequin, L Aitchison, and M Lengyel
COSYNE, 2014  

Graded memories in balanced attractor networks

D Festa, G Hennequin, and M Lengyel
COSYNE, 2014  

Transient collective dynamics in inhibition-stabilized motor circuits

G Hennequin, TP Vogels, and W Gerstner
COSYNE, 2013  

Nonnormal amplification in random balanced neuronal networks

G Hennequin, TP Vogels, and W Gerstner
COSYNE, 2012  

Plasticity and stability in recurrent neural networks

F Zenke, G Hennequin, H Sprekeler, TP Vogels, and W Gerstner
CNS, 2011  

Fast and richly structured activity in cortical networks with local inhibition

G Hennequin, TP Vogels, and W Gerstner
CNS, 2011