Guillaume Hennequin
Guillaume Hennequin
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Cited by
Cited by
Optimal control of transient dynamics in balanced networks supports generation of complex movements
G Hennequin, TP Vogels, W Gerstner
Neuron 82 (6), 1394-1406, 2014
Inhibitory Plasticity: Balance, Control, and Codependence
G Hennequin, EJ Agnes, TP Vogels
Annual Review of Neuroscience 40 (1), 2017
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector
F Zenke, G Hennequin, W Gerstner
PLoS computational biology 9 (11), e1003330, 2013
The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability
G Hennequin, Y Ahmadian, DB Rubin, M Lengyel, KD Miller
Neuron 98 (4), 846-860. e5, 2018
Motor primitives in space and time via targeted gain modulation in cortical networks
JP Stroud, MA Porter, G Hennequin, TP Vogels
Nature neuroscience 21 (12), 1774-1783, 2018
Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
R Echeveste, L Aitchison, G Hennequin, M Lengyel
Nature neuroscience 23, 1138–1149, 2020
Non-normal amplification in random balanced neuronal networks
G Hennequin, TP Vogels, W Gerstner
Physical Review E 86 (1), 011909, 2012
Fast Sampling-Based Inference in Balanced Neuronal Networks
G Hennequin, L Aitchison, M Lengyel
Advances in Neural Information Processing Systems, 2240-2248, 2014
Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model
TC Kao, MS Sadabadi, G Hennequin
Neuron, 2021
Exact natural gradient in deep linear networks and its application to the nonlinear case
A Bernacchia, M Lengyel, G Hennequin
Advances in Neural Information Processing Systems, 5945-5954, 2018
STDP in adaptive neurons gives close-to-optimal information transmission
G Hennequin, W Gerstner, JP Pfister
Frontiers in Computational Neuroscience 4, 2010
Natural continual learning: success is a journey, not (just) a destination
TC Kao, KT Jensen, GM van de Ven, A Bernacchia, G Hennequin
Thirty-Fifth Conference on Neural Information Processing Systems, 2021
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
KT Jensen, TC Kao, M Tripodi, G Hennequin
Advances in Neural Information Processing Systems 33, 2020
Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics
TC Kao, G Hennequin
Current Opinion in Neurobiology 58, 122-129, 2019
Efficient communication over complex dynamical networks: The role of matrix non-normality
G Baggio, V Rutten, G Hennequin, S Zampieri
Science Advances 6 (22), eaba2282, 2020
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
V Rutten, A Bernacchia, M Sahani, G Hennequin
Advances in Neural Information Processing Systems 33, 2020
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
K Jensen, TC Kao, J Stone, G Hennequin
Advances in Neural Information Processing Systems 34, 10613-10626, 2021
Code-specific policy gradient rules for spiking neurons
H Sprekeler, G Hennequin, W Gerstner
Advances in Neural Information Processing Systems 22, 1741-1749, 2009
iLQR-VAE: control-based learning of input-driven dynamics with applications to neural data
M Schimel, TC Kao, KT Jensen, G Hennequin
bioRxiv, 2021.10. 07.463540, 2022
Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability
L Aitchison, G Hennequin, M Lengyel
arXiv preprint arXiv:1807.08952, 2018
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