Neural Dynamics and Control Group


Peer-reviewed articles

Adaptive erasure of spurious sequences in sensory cortical circuits
A Bernacchia, József Fiser, G Hennequin* and M Lengyel*
Neuron, 2022
iLQR-VAE: control-based learning of input-driven dynamics with applications to neural data
M Schimel, TC Kao, K Jensen and G Hennequin
ILCR (oral), 2022
Optimal anticipatory control as a theory of motor preparation: a thalamocortical circuit model
TC Kao, M Sadabadi and G Hennequin
Neuron, 2021
Natural continual learning: success is a journey, not (just) a destination
TC Kao*, K Jensen*, G van de Ven, A Bernacchia and G Hennequin
NeurIPS, 2021
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
K Jensen*, TC Kao*, J Stone and G Hennequin
NeurIPS, 2021
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
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
K Jensen, TC 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
Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics
TC Kao and G Hennequin
Current Opinion in Neurobiology, 2019
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
TC 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
Exact natural gradient in deep linear networks and application to the nonlinear case
A Bernacchia, M Lengyel and G Hennequin
NeurIPS, 2018
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
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
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
Code-specific policy gradient learning rules for spiking neurons
Sprekeler H, G Hennequin and Gerstner W
NeurIPS, 2009