Synaptic depression enables neuronal gain control JS Rothman, L Cathala, V Steuber, RA Silver Nature 457 (7232), 1015-1018, 2009 | 256 | 2009 |
neuroConstruct: a tool for modeling networks of neurons in 3D space P Gleeson, V Steuber, RA Silver Neuron 54 (2), 219-235, 2007 | 221 | 2007 |
Cerebellar LTD and pattern recognition by Purkinje cells V Steuber, W Mittmann, FE Hoebeek, RA Silver, CI De Zeeuw, M Häusser, ... Neuron 54 (1), 121-136, 2007 | 187 | 2007 |
Structural features of a close homologue of L1 (CHL1) in the mouse: a new member of the L1 family of neural recognition molecules J Holm, R Hillenbrand, V Steuber, U Bartsch, M Moos, H Lübbert, ... European Journal of Neuroscience 8 (8), 1613-1629, 1996 | 138 | 1996 |
Cerebellar output controls generalized spike‐and‐wave discharge occurrence L Kros, OHJ Eelkman Rooda, JK Spanke, P Alva, MN van Dongen, ... Annals of neurology 77 (6), 1027-1049, 2015 | 137 | 2015 |
Patterns and pauses in Purkinje cell simple spike trains: experiments, modeling and theory E De Schutter, V Steuber Neuroscience 162 (3), 816-826, 2009 | 107 | 2009 |
Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells V Steuber, NW Schultheiss, RA Silver, E De Schutter, D Jaeger Journal of computational neuroscience 30, 633-658, 2011 | 68 | 2011 |
Distinctive role of KV1. 1 subunit in the biology and functions of low threshold K+ channels with implications for neurological disease SV Ovsepian, M LeBerre, V Steuber, VB O'Leary, C Leibold, JO Dolly Pharmacology & therapeutics 159, 93-101, 2016 | 66 | 2016 |
A biophysical model of synaptic delay learning and temporal pattern recognition in a cerebellar Purkinje cell V Steuber, D Willshaw Journal of computational neuroscience 17, 149-164, 2004 | 53 | 2004 |
Modeling the generation of output by the cerebellar nuclei V Steuber, D Jaeger Neural Networks 47, 112-119, 2013 | 37 | 2013 |
STD-dependent and independent encoding of input irregularity as spike rate in a computational model of a cerebellar nucleus neuron J Luthman, FE Hoebeek, R Maex, N Davey, R Adams, CI De Zeeuw, ... The Cerebellum 10, 667-682, 2011 | 35 | 2011 |
The Open Source Brain Initiative: enabling collaborative modelling in computational neuroscience P Gleeson, E Piasini, S Crook, R Cannon, V Steuber, D Jaeger, S Solinas, ... BMC neuroscience 13, 1-2, 2012 | 29 | 2012 |
A defined heteromeric KV1 channel stabilizes the intrinsic pacemaking and regulates the output of deep cerebellar nuclear neurons to thalamic targets SV Ovsepian, V Steuber, M Le Berre, L O’Hara, VB O’Leary, JO Dolly The Journal of physiology 591 (7), 1771-1791, 2013 | 25 | 2013 |
Passive models of neurons in the deep cerebellar nuclei: the effect of reconstruction errors V Steuber, E De Schutter, D Jaeger Neurocomputing 58, 563-568, 2004 | 24 | 2004 |
25th annual computational neuroscience meeting: CNS-2016 TO Sharpee, A Destexhe, M Kawato, V Sekulić, FK Skinner, DK Wójcik, ... BMC neuroscience 17, 1-112, 2016 | 22 | 2016 |
Adaptive leaky integrator models of cerebellar Purkinje cells can learn the clustering of temporal patterns V Steuber, DJ Willshaw Neurocomputing 26, 271-276, 1999 | 22 | 1999 |
The role of parvalbumin-positive interneurons in auditory steady-state response deficits in schizophrenia C Metzner, B Zurowski, V Steuber Scientific reports 9 (1), 18525, 2019 | 21 | 2019 |
Creating, documenting and sharing network models SM Crook, JA Bednar, S Berger, R Cannon, AP Davison, M Djurfeldt, ... Network: Computation in Neural Systems 23 (4), 131-149, 2012 | 20 | 2012 |
Long-term depression and recognition of parallel fibre patterns in a multi-compartmental model of a cerebellar Purkinje cell V Steuber, E De Schutter Neurocomputing 38, 383-388, 2001 | 20 | 2001 |
Dendritic morphology predicts pattern recognition performance in multi-compartmental model neurons with and without active conductances G De Sousa, R Maex, R Adams, N Davey, V Steuber Journal of computational neuroscience 38, 221-234, 2015 | 19 | 2015 |