| Simulation of networks of spiking neurons: a review of tools and strategies R Brette, M Rudolph, T Carnevale, M Hines, D Beeman, JM Bower, ... Journal of computational neuroscience 23, 349-398, 2007 | 1057 | 2007 |
| PyNN: a common interface for neuronal network simulators AP Davison, D Brüderle, JM Eppler, J Kremkow, E Muller, D Pecevski, ... Frontiers in neuroinformatics 2, 388, 2009 | 835 | 2009 |
| A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback R Legenstein, D Pecevski, W Maass PLoS computational biology 4 (10), e1000180, 2008 | 342 | 2008 |
| PCSIM: a parallel simulation environment for neural circuits fully integrated with Python D Pecevski, T Natschläger, K Schuch Frontiers in neuroinformatics 3, 356, 2009 | 148 | 2009 |
| Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons D Pecevski, L Buesing, W Maass PLoS computational biology 7 (12), e1002294, 2011 | 138 | 2011 |
| Recurrent spiking networks solve planning tasks E Rueckert, D Kappel, D Tanneberg, D Pecevski, J Peters Scientific reports 6 (1), 21142, 2016 | 84 | 2016 |
| Oger: modular learning architectures for large-scale sequential processing D Verstraeten, B Schrauwen, S Dieleman, P Brakel, P Buteneers, ... The Journal of Machine Learning Research 13 (1), 2995-2998, 2012 | 42 | 2012 |
| Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons D Probst, MA Petrovici, I Bytschok, J Bill, D Pecevski, J Schemmel, ... Frontiers in computational neuroscience 9, 13, 2015 | 33 | 2015 |
| NEVESIM: event-driven neural simulation framework with a Python interface D Pecevski, D Kappel, Z Jonke Frontiers in neuroinformatics 8, 70, 2014 | 27 | 2014 |
| Learning probabilistic inference through spike-timing-dependent plasticity D Pecevski, W Maass eneuro 3 (2), 2016 | 16 | 2016 |
| Theoretical analysis of learning with reward-modulated spike-timing-dependent plasticity D Pecevski, W Maass, R Legenstein Advances in Neural Information Processing Systems 20, 2007 | 16 | 2007 |
| NeuralEnsemble. Org: Unifying neural simulators in Python to ease the model complexity bottleneck E Muller, AP Davison, T Brizzi, D Bruederle, JM Eppler, J Kremkow, ... Frontiers in neuroscience conference abstract: Neuroinformatics 2009, 2009 | 7 | 2009 |
| PyNN–a python package for simulator-independent specification of neuronal network models A Davison, D Brüderle, J Kremkow, E Muller, D Pecevski, L Perrinet, ... | 4 | 2009 |
| NeuralEnsemble: Towards a meta-environment for network modeling and data analysis P Yger, D Bruderle, J Eppler, J Kremkow, D Pecevski, L Perrinet, ... Eight Göttingen Meeting of the German neuroscience society, T26-4C, 2009 | 2 | 2009 |
| Modelling inference and learning in biological networks of neurons DA Pecevski na, 2011 | | 2011 |
| Dendritic computation could support probabilistic inference in networks of spiking neurons R Legenstein, D Pecevski, LH Büsing, W Maass Neuroscience 2011, 2011 | | 2011 |
| Supplement to Recurrent Spiking Networks Solve Planning Tasks E Rueckert, D Kappel, D Tanneberg, D Pecevski, J Peters | | |
| Probabilistic Inference in Discrete Spaces with Networks of LIF Neurons D Probst, MA Petrovici, I Bytschok, J Bill, D Pecevski, J Schemmel, ... | | |
