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
Tim P. VogelsInstitute of Science and Technology, AustriaVerified email at ist.ac.at
Ta-Chu KaoMeta Reality LabsVerified email at meta.com
Laurence AitchisonUniversity of BristolVerified email at bristol.ac.uk
Kristopher T. JensenUniversity of CambridgeVerified email at cam.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
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)Verified email at fmi.ch
Virginia M. S. RuttenGatsby Computational Neuroscience Unit, UCL - Janelia Research Campus, HHMI - Cambridge UniversityVerified email at janelia.hhmi.org
Jake P. StroudSir Henry Wellcome Postdoctoral Fellow at the University of CambridgeVerified email at eng.cam.ac.uk
Mason PorterUCLAVerified email at math.ucla.edu
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
Mahdieh S. SadabadiAssistant Professor, Queen Mary University of LondonVerified email at qmul.ac.uk
Gido M. van de VenPostdoctoral Associate, KU LeuvenVerified email at kuleuven.be

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	269	2014
Inhibitory Plasticity: Balance, Control, and Codependence G Hennequin, EJ Agnes, TP Vogels Annual Review of Neuroscience 40 (1), 2017	170	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	163	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	118	2018
Non-normal amplification in random balanced neuronal networks G Hennequin, TP Vogels, W Gerstner Physical Review E 86 (1), 011909, 2012	81	2012
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	80	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	75	2020
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
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	44	2018
STDP in adaptive neurons gives close-to-optimal information transmission G Hennequin, W Gerstner, JP Pfister Frontiers in Computational Neuroscience 4, 2010	40	2010
Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model TC Kao, MS Sadabadi, G Hennequin Neuron, 2021	34	2021
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	22	2021
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	17	2020
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	16	2020
Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics TC Kao, G Hennequin Current Opinion in Neurobiology 58, 122-129, 2019	16	2019
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	13	2020
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
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
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	7	2021
Analog memories in a balanced rate-based network of EI neurons D Festa, G Hennequin, M Lengyel Advances in Neural Information Processing Systems 27, 2231-2239, 2014	7	2014

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