Robert A. Legenstein

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Reward-based stochastic self-configuration of neural circuits pdf

David Kappel, Robert A. Legenstein, Stefan Habenschuss, 2017, ArXiv.

A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models

Henry Markram, Robert A. Legenstein, Wolfgang Maass, 2002, Biologically Motivated Computer Vision.

Total Wire Length as a Salient Circuit Complexity Measure for Sensory Processing

Robert A. Legenstein, Wolfgang Maass, W. Maass, 2001, Electron. Colloquium Comput. Complex..

Wire length as a circuit complexity measure

Robert A. Legenstein, Wolfgang Maass, W. Maass, 2005, J. Comput. Syst. Sci..

Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets pdf

Robert A. Legenstein, Wolfgang Maass, Franz Scherr, 2019, ArXiv.

Long Term Memory and the Densest K-Subgraph Problem

Santosh S. Vempala, Christos H. Papadimitriou, Robert A. Legenstein, 2018, ITCS.

Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming

Martin Ebner, Robert A. Legenstein, Behnam Taraghi, 2016, LAK.

Neuromorphic hardware in the loop: Training a deep spiking network on the BrainScaleS wafer-scale system

Robert A. Legenstein, Wolfgang Maass, Andreas Hartel, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Neural circuits for pattern recognition with small total wire length

Robert A. Legenstein, Wolfgang Maass, 2002, Theor. Comput. Sci..

A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity

Robert A. Legenstein, Wolfgang Maass, 2005, NIPS.

2007 Special Issue: Edge of chaos and prediction of computational performance for neural circuit models

Robert A. Legenstein, Wolfgang Maass, W. Maass, 2007 .

Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning

Robert A. Legenstein, Wolfgang Maass, Steven M. Chase, 2009, NIPS.

Combining predictions for accurate recommender systems

Robert A. Legenstein, Michael Jahrer, Andreas Töscher, 2010, KDD.

Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons

Benjamin Schrauwen, Robert A. Legenstein, Lars Buesing, 2010, Neural Computation.

At the Edge of Chaos: Real-time Computations and Self-Organized Criticality in Recurrent Neural Networks

Robert A. Legenstein, Nils Bertschinger, Thomas Natschläger, 2004, NIPS.

Long short-term memory and Learning-to-learn in networks of spiking neurons pdf

Robert A. Legenstein, Wolfgang Maass, Anand Subramoney, 2018, NeurIPS.

Spiking Neurons Can Learn to Solve Information Bottleneck Problems and Extract Independent Components

Robert A. Legenstein, Wolfgang Maass, Stefan Klampfl, 2009, Neural Computation.

Optimizing the Layout of a Balanced Tree

Robert A. Legenstein, Wolfgang Maass, W. Maass, 2001, Electron. Colloquium Comput. Complex..

Embodied Synaptic Plasticity With Online Reinforcement Learning

Rüdiger Dillmann, Robert A. Legenstein, Wolfgang Maass, 2019, Front. Neurorobot..

Pattern representation and recognition with accelerated analog neuromorphic systems

Robert A. Legenstein, Wolfgang Maass, René Schüffny, 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype

Steve B. Furber, Robert A. Legenstein, Wolfgang Maass, 2019, IEEE Transactions on Biomedical Circuits and Systems.

Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment

Robert A. Legenstein, Wolfgang Maass, W. Maass, 2014, PLoS Comput. Biol..

Network Plasticity as Bayesian Inference

David Kappel, Robert A. Legenstein, Stefan Habenschuss, 2015, PLoS Comput. Biol..

Hamiltonian synaptic sampling in a model for reward-gated network plasticity pdf

David Kappel, Robert A. Legenstein, Wolfgang Maass, 2016, ArXiv.

Oscillatory background activity implements a backbone for sampling-based computations in spiking neural networks pdf

Robert A. Legenstein, Michael G. Müller, R. Legenstein, 2020, ArXiv.

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

Robert A. Legenstein, Wolfgang Maass, Dejan Pecevski, 2008, PLoS Comput. Biol..

Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons

Robert A. Legenstein, Wolfgang Maass, Stefan Klampfl, 2006, NIPS.

Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits

Robert A. Legenstein, Wolfgang Maass, Nils Bertschinger, 2004, NIPS.

Reinforcement Learning on Slow Features of High-Dimensional Input Streams

Robert A. Legenstein, Niko Wilbert, Laurenz Wiskott, 2010, PLoS Comput. Biol..

Foundations for a Circuit Complexity Theory of Sensory Processing

Robert A. Legenstein, Wolfgang Maass, W. Maass, 2000, NIPS.

Brain Computation: A Computer Science Perspective

Santosh S. Vempala, Christos H. Papadimitriou, Robert A. Legenstein, 2019, Computing and Software Science.

Computer science: Nanoscale connections for brain-like circuits

Robert A. Legenstein, R. Legenstein, 2015, Nature.

On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing

Benjamin Schrauwen, Robert A. Legenstein, Lars Buesing, 2008, NIPS.

Variable Binding through Assemblies in Spiking Neural Networks

Santosh S. Vempala, Christos H. Papadimitriou, Robert A. Legenstein, 2016, CoCo@NIPS.

Recurrent Network Models, Reservoir Computing

Robert A. Legenstein, 2014, Encyclopedia of Computational Neuroscience.

Integration of nanoscale memristor synapses in neuromorphic computing architectures

Giacomo Indiveri, Bernabé Linares-Barranco, Robert A. Legenstein, 2013, Nanotechnology.

On the Classification Capability of Sign-Constrained Perceptrons

Robert A. Legenstein, Wolfgang Maass, W. Maass, 2008, Neural Computation.

Deep Rewiring: Training very sparse deep networks pdf

David Kappel, Robert A. Legenstein, Wolfgang Maass, 2017, ICLR.

On the Complexity of Knock-knee Channel-Routing with 3-Terminal Nets

Robert A. Legenstein, 2001, Electron. Colloquium Comput. Complex..

Fast learning synapses with molecular spin valves via selective magnetic potentiation pdf

Robert A. Legenstein, Alberto Riminucci, R. Legenstein, 2019, ArXiv.

What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?

Robert A. Legenstein, Wolfgang Maass, Christian Naeger, 2005, Neural Computation.

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

Robert A. Legenstein, Wolfgang Maass, Dejan Pecevski, 2007, NIPS.

Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring

David Kappel, Robert A. Legenstein, Stefan Habenschuss, 2015, NIPS.