Publications
(see also our Google Scholar profile)
Published
Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model
T-C Kao, M Sadabadi, and G Hennequin
Neuron, 2021
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
K T Jensen, T-C Kao, M Tripodi, and G Hennequin
NeurIPS, 2020
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
V Rutten, A Bernacchia, M Sahani, and G Hennequin
NeurIPS (oral), 2020
Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
R Echeveste, L Aitchison, G Hennequin*, and M Lengyel*
Nature Neuroscience, 2020
Efficient communication over complex dynamical networks: the role of matrix non-normality
G Baggio, V Rutten, G Hennequin*, and S Zampieri*
Science Advances, 2020
Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics
TC Kao, and G Hennequin
Current Opinion in Neurobiology, 2019
Exact natural gradient in deep linear networks and application to the nonlinear case
A Bernacchia, M Lengyel, and G Hennequin
NeurIPS, 2018
Motor primitives in space and time via targeted gain modulation in cortical networks
J Stroud, G Hennequin, MA Porter, and TP Vogels
Nature Neuroscience, 2018
Information transmission in dynamical networks: the normal network case
G Baggio, V Rutten, G Hennequin, and S Zampieri
IEEE Conference on Decision and Control, 2018
Null ain't dull: new perspectives on motor cortex
T-C Kao, and G Hennequin
Trends in Cognitive Sciences, 2018
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
Analog memories in a balanced rate-based network of E/I neurons
D Festa, G Hennequin, and M Lengyel
NIPS (oral), 2014
Fast sampling-based inference in balanced neuronal networks
G Hennequin, L Aitchison, and M Lengyel
NIPS, 2014
Optimal control of transient dynamics in balanced networks supports generation of complex movements
G Hennequin, TP VogelsŦ, and W GerstnerŦ
Neuron, 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
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
K T Jensen, T-C Kao, M Tripodi, and G Hennequin
arXiv, 2020
Optimal anticipatory control as a theory of motor preparation: a thalamocortical circuit model
T-C Kao, M S Sadabadi, and G Hennequin
bioRXiv, 2020
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 (no longer updated)
Sequential components analysis
V Rutten, A Bernacchia, and G Hennequin
COSYNE, 2019
Hebb 'n' Dale: efficient coding by time-reversible dynamics in recurrent circuits
A Bernacchia, J Fiser, G Hennequin, and M Lengyel
COSYNE, 2018
Probabilistic inference emerges from learning in neural circuits with a cost on reliability
L Aitchison, G Hennequin, and M Lengyel
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
Orthogonal preparatory and movement subspaces in monkey, mouse, and model
T-C Kao, M Sadabadi, and G Hennequin
COSYNE, 2018
Flexible, optimal motor control in a thalamo-cortical circuit model
M Sadabadi, T-C Kao, and G Hennequin
COSYNE, 2018
GSM=SSN: recurrent neural circuits optimised for probabilistic inference
R Echeveste, G Hennequin*, and M Lengyel*
COSYNE, 2017
Dale's principle preserves sequentiality in neural circuits
A Bernacchia, J Fiser, 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
Cherchez les auxiliaires: interneurons are key for high-capacity attractor networks
D Festa, G Hennequin, and M Lengyel
COSYNE, 2017
Limits on fast, high-dimensional information processing in recurrent circuits
V Rutten, and G Hennequin
COSYNE, 2017
Balance out of control: robust stabilization of recurrent circuits via inhibitory plasticity
G Hennequin, and TP Vogels
COSYNE, 2016
Graded memories in balanced attractor networks
D Festa, G Hennequin, and M Lengyel
COSYNE, 2014
Fast sampling in recurrent neural circuits
G Hennequin, L Aitchison, and M Lengyel
COSYNE, 2014
The dynamics of variability in nonlinear recurrent circuits
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
Fast and richly structured activity in cortical networks with local inhibition
G Hennequin, TP Vogels, and W Gerstner
CNS, 2011
Plasticity and stability in recurrent neural networks
F Zenke, G Hennequin, H Sprekeler, TP Vogels, and W Gerstner
CNS, 2011