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Neural Computation
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September 15, 2005
What can a neuron learn with spike-timing-dependent plasticity?
Robert Legenstein, Christian Naeger, Wolfgang Maass
Neural Computation
|
December 24, 2009
Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons
Lars Büsing, Benjamin Schrauwen, Robert Legenstein
Plos Computational Biology
|
September 3, 2010
Reinforcement learning on slow features of high-dimensional input streams
Robert Legenstein, Niko Wilbert, Laurenz Wiskott
Reviews in the Neurosciences
|
August 22, 2003
Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons
Robert Legenstein, Henry Markram, Wolfgang Maass
Archives of Orthopaedic and Trauma Surgery
|
March 29, 2003
Indomethacin versus meloxicam for prevention of heterotopic ossification after total hip arthroplasty
Robert Legenstein, Peter Bösch, Alfred Ungersböck
Plos Computational Biology
|
October 11, 2008
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback
Robert Legenstein, Dejan Pecevski, Wolfgang Maass
Neural Computation
|
November 21, 2008
Spiking neurons can learn to solve information bottleneck problems and extract independent components
Stefan Klampfl, Robert Legenstein, Wolfgang Maass
Cerebral Cortex (New York, N.Y. : 1991)
|
November 14, 2012
Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning
Gregor M Hoerzer, Robert Legenstein, Wolfgang Maass
Plos One
|
March 17, 2025
Non-synaptic plasticity enables memory-dependent local learning
Romain Ferrand, Maximilian Baronig, Florian Unger, et al.
The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|
August 2, 2017
Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs
Zeno Jonke, Robert Legenstein, Stefan Habenschuss, et al.
Page
of 5
Search research articles
Search
Showing results (11-20 of 43) with videos related to
Sort By:
Page
of 5
Neural Computation
|
September 15, 2005
What can a neuron learn with spike-timing-dependent plasticity?
Robert Legenstein, Christian Naeger, Wolfgang Maass
Neural Computation
|
December 24, 2009
Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons
Lars Büsing, Benjamin Schrauwen, Robert Legenstein
Plos Computational Biology
|
September 3, 2010
Reinforcement learning on slow features of high-dimensional input streams
Robert Legenstein, Niko Wilbert, Laurenz Wiskott
Reviews in the Neurosciences
|
August 22, 2003
Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons
Robert Legenstein, Henry Markram, Wolfgang Maass
Archives of Orthopaedic and Trauma Surgery
|
March 29, 2003
Indomethacin versus meloxicam for prevention of heterotopic ossification after total hip arthroplasty
Robert Legenstein, Peter Bösch, Alfred Ungersböck
Plos Computational Biology
|
October 11, 2008
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback
Robert Legenstein, Dejan Pecevski, Wolfgang Maass
Neural Computation
|
November 21, 2008
Spiking neurons can learn to solve information bottleneck problems and extract independent components
Stefan Klampfl, Robert Legenstein, Wolfgang Maass
Cerebral Cortex (New York, N.Y. : 1991)
|
November 14, 2012
Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning
Gregor M Hoerzer, Robert Legenstein, Wolfgang Maass
Plos One
|
March 17, 2025
Non-synaptic plasticity enables memory-dependent local learning
Romain Ferrand, Maximilian Baronig, Florian Unger, et al.
The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|
August 2, 2017
Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs
Zeno Jonke, Robert Legenstein, Stefan Habenschuss, et al.
Page
of 5