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Dejan Pecevski

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

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JMLR Journal 2012 Journal Article

Oger: Modular Learning Architectures For Large-Scale Sequential Processing

David Verstraeten
Benjamin Schrauwen
Sander Dieleman
Philemon Brakel
Pieter Buteneers
Dejan Pecevski

Oger (OrGanic Environment for Reservoir computing) is a Python toolbox for building, training and evaluating modular learning architectures on large data sets. It builds on MDP for its modularity, and adds processing of sequential data sets, gradient descent training, several cross-validation schemes and parallel parameter optimization methods. Additionally, several learning algorithms are implemented, such as different reservoir implementations (both sigmoid and spiking), ridge regression, conditional restricted Boltzmann machine (CRBM) and others, including GPU accelerated versions. Oger is released under the GNU LGPL, and is available from http://organic.elis.ugent.be/oger. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2012. ( edit, beta )

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NeurIPS Conference 2007 Conference Paper

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

Dejan Pecevski
Wolfgang Maass
Robert Legenstein

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 relevant adaptive changes in com- plex networks of spiking neurons. However the potential and limitations of this learning rule could so far only be tested through computer simulations. This ar- ticle provides tools for an analytic treatment of reward-modulated STDP, which allow us to predict under which conditions reward-modulated STDP will be able to achieve a desired learning effect. In particular, we can produce in this way a theoretical explanation and a computer model for a fundamental experimental ﬁnding on biofeedback in monkeys (reported in [1]).

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