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The Journal of Neuroscience : the Official Journal of the Society for Neuroscience|July 29, 2011
Branch-specific plasticity enables self-organization of nonlinear computation in single neuronsRobert Legenstein, Wolfgang MaassNeural Networks : the Official Journal of the International Neural Network Society|May 23, 2007
Edge of chaos and prediction of computational performance for neural circuit modelsRobert Legenstein, Wolfgang MaassNeural Computation|November 30, 2007
On the classification capability of sign-constrained perceptronsRobert Legenstein, Wolfgang MaassPlos Computational Biology|October 24, 2014
Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environmentRobert Legenstein, Wolfgang MaassNeural Computation|September 15, 2005
What can a neuron learn with spike-timing-dependent plasticity?Robert Legenstein, Christian Naeger, Wolfgang MaassPlos Computational Biology|October 11, 2008
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedbackRobert Legenstein, Dejan Pecevski, Wolfgang MaassNeural Computation|November 21, 2008
Spiking neurons can learn to solve information bottleneck problems and extract independent componentsStefan Klampfl, Robert Legenstein, Wolfgang MaassReviews in the Neurosciences|August 22, 2003
Input prediction and autonomous movement analysis in recurrent circuits of spiking neuronsRobert Legenstein, Henry Markram, Wolfgang MaassCerebral Cortex (New York, N.Y. : 1991)|November 14, 2012
Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learningGregor M Hoerzer, Robert Legenstein, Wolfgang MaassThe Journal of Neuroscience : the Official Journal of the Society for Neuroscience|August 2, 2017
Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit MotifsZeno Jonke, Robert Legenstein, Stefan Habenschuss, et al.Pageof 10