Guillaume Hennequin

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Máté LengyelProfessor of Computational Neuroscience, University of Cambridge; Senior Research Fellow, CEUVerified email at eng.cam.ac.uk
Wulfram GerstnerEPFLVerified email at epfl.ch
Ta-Chu KaoMeta Reality LabsVerified email at meta.com
Tim P. VogelsInstitute of Science and Technology, AustriaVerified email at ist.ac.at
Kristopher T. JensenUniversity of CambridgeVerified email at cam.ac.uk
Laurence AitchisonUniversity of BristolVerified email at bristol.ac.uk
Alberto BernacchiaResearch Scientist, University of CambridgeVerified email at cam.ac.uk
Yashar AhmadianUniversity of Oregon, Departments of Biology and MathematicsVerified email at uoregon.edu
Kenneth D. MillerProfessor of Neuroscience, Columbia UniversityVerified email at columbia.edu
Daniel B. RubinInstructor in Neurology, Harvard Medical SchoolVerified email at mgh.harvard.edu
Rodrigo EchevesteResearch Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICETVerified email at sinc.unl.edu.ar
Everton J. AgnesBiozentrum, University of BaselVerified email at unibas.ch
Friedemann ZenkeFriedrich Miescher Institute (FMI), University of BaselVerified email at fmi.ch
Virginia M. S. RuettenGatsby Computational Neuroscience Unit, UCL - Janelia Research Campus, HHMI - Cambridge UniversityVerified email at janelia.hhmi.org
Jake P. StroudSir Henry Wellcome Postdoctoral Research Fellow at the University of CambridgeVerified email at eng.cam.ac.uk
Mason PorterUCLAVerified email at math.ucla.edu
Mahdieh S. SadabadiAssistant Professor, University of ManchesterVerified email at manchester.ac.uk
Sandro ZampieriUniversita' di PadovaVerified email at dei.unipd.it
Giacomo BaggioAssistant Professor, University of PadovaVerified email at dei.unipd.it
Jean-Pascal PfisterProfessor of Theoretical Neuroscience, Institute of Neuroinformatics, University of Zurich and ETHVerified email at ini.uzh.ch

Guillaume Hennequin

The University of Cambridge, UK

Verified email at cam.ac.uk - Homepage

Computational neuroscience and machine learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	296	2014
Inhibitory Plasticity: Balance, Control, and Codependence G Hennequin, EJ Agnes, TP Vogels Annual Review of Neuroscience 40 (1), 2017	188	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	169	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	131	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	91	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	87	2020
Non-normal amplification in random balanced neuronal networks G Hennequin, TP Vogels, W Gerstner Physical Review E 86 (1), 011909, 2012	86	2012
Fast Sampling-Based Inference in Balanced Neuronal Networks G Hennequin, L Aitchison, M Lengyel Advances in Neural Information Processing Systems, 2240-2248, 2014	54	2014
Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model TC Kao, MS Sadabadi, G Hennequin Neuron, 2021	50	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	49	2018
STDP in adaptive neurons gives close-to-optimal information transmission G Hennequin, W Gerstner, JP Pfister Frontiers in Computational Neuroscience 4, 2010	42	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	30	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	23	2020
Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics TC Kao, G Hennequin Current Opinion in Neurobiology 58, 122-129, 2019	20	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	19	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	18	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	11	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	10	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	8	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	8	2018

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