Richard Naud

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

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	1460	2014
A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature Neuroscience 22 (11), 1761-1770, 2019	594	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	331	2008
How good are neuron models? W Gerstner, R Naud Science 326 (5951), 379, 2009	301	2009
Temporal whitening by power-law adaptation in neocortical neurons C Pozzorini, R Naud, S Mensi, W Gerstner Nature neuroscience 16 (7), 942-948, 2013	185	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	173	2008
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	155	2021
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	147	2008
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	110	2012
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	99	2018
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	79	2015
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	62	2020
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	55	2012
Improved Similarity Measures for Small Sets of Spike Trains R Naud, F Gerhard, S Mensi, W Gerstner Neural Computation 23 (12), 3016-3069, 2011	47	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	43	2014
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	39	2021
Classes of dendritic information processing A Payeur, JC Béïque, R Naud Current Opinion in Neurobiology 58, 78-85, 2019	36	2019
Spike-timing prediction in cortical neurons with active dendrites R Naud, B Bathellier, W Gerstner Frontiers in Computational Neuroscience 8, 90, 2014	32	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	32	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	29	2011

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