Richard Naud

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Wulfram GerstnerEPFLVerified email at epfl.ch
Alexandre PayeurResearch associate at Université de MontréalVerified email at umontreal.ca
Jean-Claude BeiqueProfessor, University of OttawaVerified email at uottawa.ca
Liam PaninskiColumbia UniversityVerified email at stat.columbia.edu
Christian PozzoriniPostdoctoral Fellow, Laboratory of Computational Neuroscience, EPFLVerified email at epfl.ch
Blake RichardsMila + McGill UniversityVerified email at mila.quebec
Friedemann ZenkeFriedrich Miescher Institute (FMI), University of BaselVerified email at fmi.ch
Emerson F. HarkinUniversity of Ottawa Centre for Neural DynamicsVerified email at uottawa.ca
Renaud B. JolivetMaastricht UniversityVerified email at maastrichtuniversity.nl
Jordan GuerguievUniversity of Toronto ScarboroughVerified email at mail.utoronto.ca
Henning SprekelerTechnische Universität BerlinVerified email at tu-berlin.de
Matthew LarkumHumboldt University of BerlinVerified email at hu-berlin.de
Moritz DrükePhD Student, Larkum Lab, Humboldt-Universität zu BerlinVerified email at hu-berlin.de
Jiyun N ShinNew York University School of MedicineVerified email at nyulangone.org
Guy DoronBayer AGVerified email at bayer.com
Carl PetersenEcole Polytechnique Fédérale de Lausanne (EPFL)Verified email at epfl.ch
Naoya TakahashiInterdisciplinary Institute for Neuroscience, University of BordeauxVerified email at u-bordeaux.fr
Brice BathellierInstitut de l'Audition - Institut Pasteur - CNRSVerified email at cnrs.fr
Shigeru ShinomotoATR Institute InternationalVerified email at scphys.kyoto-u.ac.jp
Zachary FriedenbergerPhysics Graduate Student, University of OttawaVerified email at uottawa.ca

Richard Naud

Department of Cellular and Molecular Medicine, Department of Physics, University of Ottawa

Verified email at uottawa.ca - Homepage

Neuroscience Computational Neuroscience Theoretical Neuroscience


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Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition W Gerstner, WM Kistler, R Naud, L Paninski Cambridge University Press, 2014	2212	2014
A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature Neuroscience 22 (11), 1761-1770, 2019	906	2019
Firing patterns in the adaptive exponential integrate-and-fire model R Naud, N Marcille, C Clopath, W Gerstner Biological cybernetics 99 (4), 335-347, 2008	395	2008
How good are neuron models? W Gerstner, R Naud Science 326 (5951), 379, 2009	330	2009
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits A Payeur, J Guerguiev, F Zenke, BA Richards, R Naud Nature Neuroscience, 1-10, 2021	260	2021
Temporal whitening by power-law adaptation in neocortical neurons C Pozzorini, R Naud, S Mensi, W Gerstner Nature neuroscience 16 (7), 942-948, 2013	213	2013
A benchmark test for a quantitative assessment of simple neuron models R Jolivet, R Kobayashi, A Rauch, R Naud, S Shinomoto, W Gerstner Journal of Neuroscience Methods 169 (2), 417-424, 2008	188	2008
The quantitative single-neuron modeling competition R Jolivet, F Schürmann, TK Berger, R Naud, W Gerstner, A Roth Biological cybernetics 99 (4), 417-426, 2008	163	2008
Sparse bursts optimize information transmission in a multiplexed neural code R Naud, H Sprekeler Proceedings of the National Academy of Sciences 115 (27), E6329-E6338, 2018	130	2018
Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms S Mensi, R Naud, C Pozzorini, M Avermann, CCH Petersen, W Gerstner Journal of neurophysiology 107 (6), 1756-1775, 2012	125	2012
Perirhinal input to neocortical layer 1 controls learning G Doron, JN Shin, N Takahashi, M Drüke, C Bocklisch, S Skenderi, ... Science 370 (6523), eaaz3136, 2020	111	2020
Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models C Pozzorini, S Mensi, O Hagens, R Naud, C Koch, W Gerstner PLOS Comput Biol 11 (6), e1004275, 2015	98	2015
Visualizing a joint future of neuroscience and neuromorphic engineering F Zenke, SM Bohté, C Clopath, IM Comşa, J Göltz, W Maass, ... Neuron 109 (4), 571-575, 2021	80	2021
Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram R Naud, W Gerstner PLOS Computational Biology 8 (10), e1002711, 2012	62	2012
Classes of dendritic information processing A Payeur, JC Béïque, R Naud Current Opinion in Neurobiology 58, 78-85, 2019	61	2019
Improved Similarity Measures for Small Sets of Spike Trains R Naud, F Gerhard, S Mensi, W Gerstner Neural Computation 23 (12), 3016-3069, 2011	49	2011
Fluctuations and information filtering in coupled populations of spiking neurons with adaptation M Deger, T Schwalger, R Naud, W Gerstner Physical Review E 90 (6), 062704, 2014	48	2014
Spike-timing prediction in cortical neurons with active dendrites R Naud, B Bathellier, W Gerstner Frontiers in Computational Neuroscience 8, 90, 2014	40	2014
Speed-invariant encoding of looming object distance requires power law spike rate adaptation SE Clarke, R Naud, A Longtin, L Maler Proceedings of the National Academy of Sciences 110 (33), 13624-13629, 2013	35	2013
From stochastic nonlinear integrate-and-fire to generalized linear models S Mensi, R Naud, W Gerstner Advances in Neural Information Processing Systems 24, 2011	33	2011

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