Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition W Gerstner, WM Kistler, R Naud, L Paninski Cambridge University Press, 2014 | 2044 | 2014 |
A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature Neuroscience 22 (11), 1761-1770, 2019 | 832 | 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 | 380 | 2008 |
How good are neuron models? W Gerstner, R Naud Science 326 (5951), 379, 2009 | 323 | 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 | 234 | 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 | 209 | 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 | 165 | 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 | 123 | 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 | 120 | 2018 |
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 | 100 | 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 | 94 | 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 | 72 | 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 | 60 | 2012 |
Classes of dendritic information processing A Payeur, JC Béïque, R Naud Current Opinion in Neurobiology 58, 78-85, 2019 | 56 | 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 | 51 | 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 | 39 | 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 | 30 | 2011 |