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Dejan Pecevski
Dejan Pecevski
AI/ML Department, Jumio Corp., Vienna, Austria
Verified email at jumio.com
Title
Cited by
Cited by
Year
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 (3), 349-398, 2007
9332007
PyNN: a common interface for neuronal network simulators
AP Davison, D Brüderle, JM Eppler, J Kremkow, E Muller, D Pecevski, ...
Frontiers in neuroinformatics 2, 11, 2009
6772009
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
2802008
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
1222011
PCSIM: a parallel simulation environment for neural circuits fully integrated with Python
D Pecevski, T Natschläger, K Schuch
Frontiers in neuroinformatics, 11, 2009
1112009
Recurrent spiking networks solve planning tasks
E Rueckert, D Kappel, D Tanneberg, D Pecevski, J Peters
Scientific reports 6 (1), 1-10, 2016
582016
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
402012
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
332015
NEVESIM: event-driven neural simulation framework with a Python interface
D Pecevski, D Kappel, Z Jonke
Frontiers in neuroinformatics 8, 70, 2014
222014
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
152007
Learning probabilistic inference through spike-timing-dependent plasticity
D Pecevski, W Maass
eneuro 3 (2), 2016
132016
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
72009
PyNN–a python package for simulator-independent specification of neuronal network models
A Davison, D Brüderle, J Kremkow, E Muller, D Pecevski, L Perrinet, ...
42009
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
22009
Modelling inference and learning in biological networks of neurons
DA Pecevski
na, 2011
2011
SBP Review PyNN
ME Martone, AP Davison, AP Davison, D Brüderle, JM Eppler, J Kremkow, ...
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, ...
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