Dejan Pecevski

A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback (2008)

Legenstein, Robert, Pecevski, Dejan, Maass, Wolfgang

Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of...

J Comput Neurosci DOI 10.1007/s10827-007-0038-6 TOPICAL REVIEW ON TECHNIQUES Simulation of networks of spiking neurons: A review of tools and strategies (2008)

Romain Brette, Michelle Rudolph, Ted Carnevale, Michael Hines, David Beeman, James M. Bower, ...

Abstract We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We...

Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity (2008)

Robert Legenstein, Dejan Pecevski, Wolfgang Maass

Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how local learning rules at single synapses support behaviorally...

PyNN: a common interface for neuronal network simulators (2008)

Davison, Andrew P., Brüderle, Daniel, Eppler, Jochen, Kremkow, Jens, Muller, Eilif, Pecevski, Dejan, ...

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its...

Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity (2007)

Legenstein, Robert, Pecevski, Dejan, Maass, Wolfgang

Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how local learning rules at single synapses support behaviorally...

A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

Legenstein, Robert, Pecevski, Dejan, Maass, Wolfgang

Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of...

PyNN: A Common Interface for Neuronal Network Simulators

Davison, Andrew P., Brüderle, Daniel, Eppler, Jochen, Kremkow, Jens, Muller, Eilif, Pecevski, Dejan, ...

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its...

PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python

Pecevski, Dejan, Natschläger, Thomas, Schuch, Klaus

The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons....