Guillaume Hennequin

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Máté LengyelProfessor of Computational Neuroscience, University of Cambridge; Senior Research Fellow, CEUVerified email at eng.cam.ac.uk
Ta-Chu KaoMeta Reality LabsVerified email at meta.com
Wulfram GerstnerEPFLVerified email at epfl.ch
Tim P. VogelsInstitute of Science and Technology, AustriaVerified email at ist.ac.at
Kristopher T. JensenUniversity College LondonVerified email at ucl.ac.uk
Laurence AitchisonUniversity of BristolVerified email at bristol.ac.uk
Alberto BernacchiaDirector of AI Research at MediaTek Research UKVerified email at mtkresearch.com
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
Virginia M. S. RuettenGatsby Computational Neuroscience Unit, UCL - Janelia Research Campus, HHMI - Cambridge UniversityVerified email at janelia.hhmi.org
Everton J. AgnesBiozentrum, University of BaselVerified email at unibas.ch
Friedemann ZenkeFriedrich Miescher Institute (FMI), University of BaselVerified email at fmi.ch
Jake P. StroudComputational Neuroscientist and Data Scientist at Opto BiosystemsVerified email at cam.ac.uk
Mason PorterUCLAVerified email at math.ucla.edu
Marine SchimelStanford UniversityVerified email at stanford.edu
Mahdieh S. SadabadiAssistant Professor, The 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

Guillaume Hennequin

Professor of Computational Neuroscience, 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	336	2014
Inhibitory Plasticity: Balance, Control, and Codependence G Hennequin, EJ Agnes, TP Vogels Annual Review of Neuroscience 40 (1), 2017	219	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	180	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	153	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	124	2020
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	110	2018
Non-normal amplification in random balanced neuronal networks G Hennequin, TP Vogels, W Gerstner Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 86 (1 …, 2012	103	2012
Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model TC Kao, MS Sadabadi, G Hennequin Neuron, 2021	83	2021
Fast Sampling-Based Inference in Balanced Neuronal Networks G Hennequin, L Aitchison, M Lengyel Advances in Neural Information Processing Systems, 2240-2248, 2014	73	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	56	2018
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	47	2021
STDP in adaptive neurons gives close-to-optimal information transmission G Hennequin, W Gerstner, JP Pfister Frontiers in Computational Neuroscience 4, 2010	44	2010
Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics TC Kao, G Hennequin Current Opinion in Neurobiology 58, 122-129, 2019	35	2019
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	32	2020
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	30	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	23	2020
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	19	2022
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	18	2021
A recurrent network model of planning explains hippocampal replay and human behavior KT Jensen, G Hennequin, MG Mattar Nature Neuroscience, 1-9, 2024	14	2024
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	10	2018

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