Suivre
Richard Naud
Richard Naud
Department of Cellular and Molecular Medicine, Department of Physics, University of Ottawa
Adresse e-mail validée de uottawa.ca - Page d'accueil
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Année
Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition
W Gerstner, WM Kistler, R Naud, L Paninski
Cambridge University Press, 2014
23762014
A deep learning framework for neuroscience
BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ...
Nature Neuroscience 22 (11), 1761-1770, 2019
9782019
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
4072008
How good are neuron models?
W Gerstner, R Naud
Science 326 (5951), 379, 2009
3352009
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
2902021
Temporal whitening by power-law adaptation in neocortical neurons
C Pozzorini, R Naud, S Mensi, W Gerstner
Nature neuroscience 16 (7), 942-948, 2013
2192013
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
1892008
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
1642008
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
1352018
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
1302012
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
1192020
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
1022015
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
862021
Classes of dendritic information processing
A Payeur, JC Béïque, R Naud
Current Opinion in Neurobiology 58, 78-85, 2019
672019
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
632012
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
492014
Improved Similarity Measures for Small Sets of Spike Trains
R Naud, F Gerhard, S Mensi, W Gerstner
Neural Computation 23 (12), 3016-3069, 2011
492011
Spike-timing prediction in cortical neurons with active dendrites
R Naud, B Bathellier, W Gerstner
Frontiers in Computational Neuroscience 8, 90, 2014
412014
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
352013
From stochastic nonlinear integrate-and-fire to generalized linear models
S Mensi, R Naud, W Gerstner
Advances in Neural Information Processing Systems 24, 2011
342011
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