Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity. - Robert Legenstein; Dejan Pecevski; Wolfgang Maass 001 (NIPS, 2007)

Simulation of networks of spiking neurons: A review of tools and strategies. - Romain Brette; Michelle Rudolph; Ted Carnevale; Michael L. Hines; David Beeman; James M. Bower; Markus Diesmann; Abigail Morrison; Philip H. Goodman; Frederick C. Harris Jr.; Milind Zirpe; Thomas Natschläger; et al. (J. Comput. Neurosci., 2007)

PyNN: a common interface for neuronal network simulators. - Andrew P. Davison; Daniel Brüderle; Jochen M. Eppler; Jens Kremkow; Eilif B. Müller; Dejan Pecevski; Laurent U. Perrinet; Pierre Yger (Frontiers Neuroinformatics, 2008)

A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback. - Robert Legenstein; Dejan Pecevski; Wolfgang Maass 001 (PLoS Comput. Biol., 2008)

PCSIM: a parallel simulation environment for neural circuits fully integrated with Python. - Dejan Pecevski; Thomas Natschläger; Klaus Schuch (Frontiers Neuroinformatics, 2009)

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons. - Dejan Pecevski; Lars Buesing; Wolfgang Maass 001 (PLoS Comput. Biol., 2011)

Oger: modular learning architectures for large-scale sequential processing. - David Verstraeten; Benjamin Schrauwen; Sander Dieleman; Philemon Brakel; Pieter Buteneers; Dejan Pecevski (J. Mach. Learn. Res., 2012)

NEVESIM: event-driven neural simulation framework with a Python interface. - Dejan Pecevski; David Kappel; Zeno Jonke (Frontiers Neuroinformatics, 2014)

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons. - Dimitri Probst; Mihai A. Petrovici; Ilja Bytschok; Johannes Bill; Dejan Pecevski; Johannes Schemmel; Karlheinz Meier (Frontiers Comput. Neurosci., 2015)