Guillaume Hennequin 个人学术档案

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Máté LengyelProfessor of Computational Neuroscience, University of Cambridge; Senior Research Fellow, CEU在 eng.cam.ac.uk 的电子邮件经过验证
Ta-Chu KaoMeta Reality Labs在 meta.com 的电子邮件经过验证
Wulfram GerstnerEPFL在 epfl.ch 的电子邮件经过验证
Tim P. VogelsInstitute of Science and Technology, Austria在 ist.ac.at 的电子邮件经过验证
Kristopher T. JensenUniversity College London在 ucl.ac.uk 的电子邮件经过验证
Laurence AitchisonUniversity of Bristol在 bristol.ac.uk 的电子邮件经过验证
Alberto BernacchiaDirector of AI Research at MediaTek Research UK在 mtkresearch.com 的电子邮件经过验证
Yashar AhmadianUniversity of Oregon, Departments of Biology and Mathematics在 uoregon.edu 的电子邮件经过验证
Kenneth D. MillerProfessor of Neuroscience, Columbia University在 columbia.edu 的电子邮件经过验证
Daniel B. RubinInstructor in Neurology, Harvard Medical School在 mgh.harvard.edu 的电子邮件经过验证
Rodrigo EchevesteResearch Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET在 sinc.unl.edu.ar 的电子邮件经过验证
Everton J. AgnesBiozentrum, University of Basel在 unibas.ch 的电子邮件经过验证
Virginia M. S. RuettenGatsby Computational Neuroscience Unit, UCL - Janelia Research Campus, HHMI - Cambridge University在 janelia.hhmi.org 的电子邮件经过验证
Friedemann ZenkeFriedrich Miescher Institute (FMI), University of Basel在 fmi.ch 的电子邮件经过验证
Jake P. StroudComputational Neuroscientist and Data Scientist at Opto Biosystems在 cam.ac.uk 的电子邮件经过验证
Mason PorterUCLA在 math.ucla.edu 的电子邮件经过验证
Marine SchimelStanford University在 stanford.edu 的电子邮件经过验证
Mahdieh S. SadabadiAssistant Professor, The University of Manchester在 manchester.ac.uk 的电子邮件经过验证
Sandro ZampieriUniversita' di Padova在 dei.unipd.it 的电子邮件经过验证
Giacomo BaggioAssistant Professor, University of Padova在 dei.unipd.it 的电子邮件经过验证

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Guillaume Hennequin

Professor of Computational Neuroscience, University of Cambridge, UK

在 cam.ac.uk 的电子邮件经过验证 - 首页

Computational neuroscience and machine learning


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	335	2014
Inhibitory Plasticity: Balance, Control, and Codependence G Hennequin, EJ Agnes, TP Vogels Annual Review of Neuroscience 40 (1), 2017	217	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	151	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	121	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	81	2021
Fast Sampling-Based Inference in Balanced Neuronal Networks G Hennequin, L Aitchison, M Lengyel Advances in Neural Information Processing Systems, 2240-2248, 2014	71	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	45	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	29	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	22	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	18	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	10	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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