Neural Dynamics and Control Group


Peer-reviewed articles

A recurrent network model of planning explains hippocampal replay and human behavior
Jensen KT, Hennequin G* and Mattar MG*
Nature Neuroscience, 2024
Learning interpretable control inputs and dynamics underlying animal locomotion
Thomas Soares Mullen, Schimel M, Hennequin G, Christian Machens, Michael Orger and Adrien Jouary
ICLR, 2024
When and why does motor preparation arise in recurrent neural network models of motor control?
Schimel M, Kao TC and Hennequin G
eLife, 2023
Fisher-Legendre (FishLeg) optimization of deep neural networks
Garcia JR*, Freddi F*, Fotiadis S, Li M, Vakili S, Bernacchia A* and Hennequin G*
ICLR (notable 25%), 2023
Adaptive erasure of spurious sequences in sensory cortical circuits
Bernacchia A, Fiser J, Hennequin G* and Lengyel M*
Neuron, 2022
iLQR-VAE: control-based learning of input-driven dynamics with applications to neural data
Schimel M, Kao TC, Jensen KT and Hennequin G
ILCR (oral), 2022
Optimal anticipatory control as a theory of motor preparation: a thalamocortical circuit model
Kao TC, Sadabadi M and Hennequin G
Neuron, 2021
Natural continual learning: success is a journey, not (just) a destination
Kao TC*, Jensen KT*, van de Ven G, Bernacchia A and Hennequin G
NeurIPS, 2021
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
Jensen KT*, Kao TC*, Stone J and Hennequin G
NeurIPS, 2021
Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
Echeveste R, Aitchison L, Hennequin G* and Lengyel M*
Nature Neuroscience, 2020
Efficient communication over complex dynamical networks: the role of matrix non-normality
Baggio G, Ruetten V, Hennequin G* and Zampieri S*
Science Advances, 2020
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
Jensen KT, Kao TC, Tripodi M and Hennequin G
NeurIPS, 2020
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
Ruetten V, Bernacchia A, Sahani M and Hennequin G
NeurIPS (oral), 2020
Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics
Kao TC and Hennequin G
Current Opinion in Neurobiology, 2019
Motor primitives in space and time via targeted gain modulation in cortical networks
Stroud J, Hennequin G, Porter MA and Vogels TP
Nature Neuroscience, 2018
Information transmission in dynamical networks: the normal network case
Baggio G, Ruetten V, Hennequin G and Zampieri S
IEEE Conference on Decision and Control, 2018
Null ain’t dull: new perspectives on motor cortex
Kao TC and Hennequin G
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
Hennequin G, Ahmadian Y*, Rubin DB*, Lengyel MŦ and Miller KDŦ
Neuron, 2018
Exact natural gradient in deep linear networks and application to the nonlinear case
Bernacchia A, Lengyel M and Hennequin G
NeurIPS, 2018
Inhibitory plasticity: balance, control, and codependence
Hennequin G*, Agnes EJ* and Vogels TP
Annu. Rev. Neurosci., 2017
Optimal control of transient dynamics in balanced networks supports generation of complex movements
Hennequin G, Vogels TPŦ and Gerstner GŦ
Neuron, 2014
Analog memories in a balanced rate-based network of E/I neurons
Festa D, Hennequin G and Lengyel M
NIPS (oral), 2014
Fast sampling-based inference in balanced neuronal networks
Hennequin G, Aitchison L and Lengyel M
NIPS, 2014
Synaptic plasticity in neural networks needs homeostasis with a fast rate detector
Zenke F, Hennequin G and Gerstner W
PLoS Computational Biology, 2013
Nonnormal amplification in random balanced neuronal networks
Hennequin G, Vogels TP and Gerstner W
Phys. Rev. E, 2012
STDP in adaptive neurons gives close-to-optimal information transmission
Hennequin G, Gerstner W and Pfister JP
Frontiers in Computational Neuroscience, 2010
Code-specific policy gradient learning rules for spiking neurons
Sprekeler H, Hennequin G and Gerstner W
NeurIPS, 2009