PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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PASCAL Network

Full NameRobert Legenstein
TitleAssociate Prof
SiteGraz University of Technology, Institute for Theoretical Computer Science (IGI)
StatusResearcher
Tel+ 43 316 873 5824
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Publications

Number of records: 19.

A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity Robert Legenstein and Wolfgang Maass Conference or Workshop Item Item not available online. (2005)
A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback Robert Legenstein, Dejan Pecevski and Wolfgang Maass Article Item not available online. (October 2008)
A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task Robert Legenstein, Steven M. Chase, Andrew B. Schwartz and Wolfgang Maass Article Item not available online. (August 2009)
At the Edge of Chaos: Real-time Computations and self-organized Criticality in Recurrent Neural Networks Thomas Natschlaeger, Nils Bertschinger and Robert Legenstein Conference or Workshop Item Item not available online. (2005)
Combining Predictions for an accurate Recommender System Michael Jahrer, Andreas Töscher and Robert Legenstein Conference or Workshop Item Item not available online. (January 2010)
Edge of Chaos and Prediction of Computational Performance for Neural Circuit Models Robert Legenstein and Wolfgang Maass Article Item availablity restricted. (2007)
Edge of chaos and prediction of computational power for neural microcircuit models Robert Legenstein and Wolfgang Maass Article Item not available online. (2005)
Emergence of Complex Computational Structures From Chaotic Neural Networks Through Reward-Modulated Hebbian Learning Gregor Hörzer, Robert Legenstein and Wolfgang Maass Article Item not available online. (11 November 2012)
Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning Robert Legenstein, Steven M. Chase, Andrew B. Schwartz and Wolfgang Maass Conference or Workshop Item Item availablity restricted. (June 2009)
Improved feature extraction by pyramidal cells through relaxed lateral inhibition Zeno Jonke, Stefan Habenschuss, Robert Legenstein and Wolfgang Maass Conference or Workshop Item Item not available online. (14 October 2012)
Improved neighborhood-based algorithms for large-scale recommender systems. Andreas Töscher, Michael Jahrer and Robert Legenstein Conference or Workshop Item Item not available online. (August 2008)
Information bottleneck optimization and independent component extraction with spiking neurons Stefan Klampfl, Robert Legenstein and Wolfgang Maass Conference or Workshop Item Item not available online. (December 2006)
Methods for estimating the computational power and generalization capability of neural microcircuits Wolfgang Maass, Robert Legenstein and Nils Bertschinger Article (2005)
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing Benjamin Schrauwen, Lars Büsing and Robert Legenstein Conference or Workshop Item Item not available online. (February 2009)
On the classification capability of sign-constrained perceptrons. Robert Legenstein and Wolfgang Maass Article Item not available online. (2007)
Spiking neurons can learn to solve information bottleneck problems and to extract independent components Stefan Klampfl, Robert Legenstein and Wolfgang Maass Article Item availablity restricted. (08 January 2007)
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity Robert Legenstein, Dejan Pecevski and Wolfgang Maass Conference or Workshop Item Item availablity restricted. (December 2007)
What can a Neuron Learn with Spike-Timing-Dependent Plasticity? Robert Legenstein, Christian Näger and Wolfgang Maass Article Item availablity restricted. (2005)
What makes a dynamical system computationally powerful? Robert Legenstein and Wolfgang Maass Book Section Item availablity restricted. (2005)

This list was generated on Mon May 20 05:32:51 BST 2013.