Robert Legenstein

Orcid: 0000-0002-8724-5507

Affiliations:
  • Graz University of Technology, Austria


According to our database1, Robert Legenstein authored at least 67 papers between 2000 and 2024.

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Bibliography

2024
Adversarially Robust Spiking Neural Networks Through Conversion.
Trans. Mach. Learn. Res., 2024

Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation.
CoRR, 2024

Adaptive Robotic Arm Control with a Spiking Recurrent Neural Network on a Digital Accelerator.
CoRR, 2024

Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing.
CoRR, 2024

Preserving Real-World Robustness of Neural Networks Under Sparsity Constraints.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

2023
Editorial: Focus on algorithms for neuromorphic computing.
Neuromorph. Comput. Eng., September, 2023

Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models.
IEEE Trans. Pattern Anal. Mach. Intell., August, 2023

Quantized rewiring: hardware-aware training of sparse deep neural networks.
Neuromorph. Comput. Eng., June, 2023

Fault Pruning: Robust Training of Neural Networks with Memristive Weights.
Proceedings of the Unconventional Computation and Natural Computation, 2023

Context-Dependent Computations in Spiking Neural Networks with Apical Modulation.
Proceedings of the Artificial Neural Networks and Machine Learning, 2023

Interaction of Generalization and Out-of-Distribution Detection Capabilities in Deep Neural Networks.
Proceedings of the Artificial Neural Networks and Machine Learning, 2023

2022
Cortical oscillations support sampling-based computations in spiking neural networks.
PLoS Comput. Biol., 2022

Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity.
CoRR, 2022

Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Spike-Based Symbolic Computations on Bit Strings and Numbers.
Proceedings of the Neuro-Symbolic Artificial Intelligence: The State of the Art, 2021

Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling.
Proceedings of the 38th International Conference on Machine Learning, 2021

Dynamic Action Inference with Recurrent Spiking Neural Networks.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

2020
The location of the axon initial segment affects the bandwidth of spike initiation dynamics.
PLoS Comput. Biol., 2020

Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring.
Frontiers Comput. Neurosci., 2020

Oscillatory background activity implements a backbone for sampling-based computations in spiking neural networks.
CoRR, 2020

H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Brain Computation: A Computer Science Perspective.
Proceedings of the Computing and Software Science - State of the Art and Perspectives, 2019

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype.
IEEE Trans. Biomed. Circuits Syst., 2019

Embodied Synaptic Plasticity With Online Reinforcement Learning.
Frontiers Neurorobotics, 2019

Fast learning synapses with molecular spin valves via selective magnetic potentiation.
CoRR, 2019

Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets.
CoRR, 2019

2018
Long short-term memory and Learning-to-learn in networks of spiking neurons.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Long Term Memory and the Densest K-Subgraph Problem.
Proceedings of the 9th Innovations in Theoretical Computer Science Conference, 2018

Deep Rewiring: Training very sparse deep networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System.
CoRR, 2017

Pattern representation and recognition with accelerated analog neuromorphic systems.
CoRR, 2017

Reward-based stochastic self-configuration of neural circuits.
CoRR, 2017



2016
Hamiltonian synaptic sampling in a model for reward-gated network plasticity.
CoRR, 2016

Variable Binding through Assemblies in Spiking Neural Networks.
Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016 co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016), 2016

Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming.
Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, 2016

2015
Network Plasticity as Bayesian Inference.
PLoS Comput. Biol., 2015

Computer science: Nanoscale connections for brain-like circuits.
Nat., 2015

Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Recurrent Network Models, Reservoir Computing.
Proceedings of the Encyclopedia of Computational Neuroscience, 2014

Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment.
PLoS Comput. Biol., 2014

2013
Integration of nanoscale memristor synapses in neuromorphic computing architectures
CoRR, 2013

2011
Editorial: One Year as EiC, and Editorial-Board Changes at TNN.
IEEE Trans. Neural Networks, 2011

2010
Reinforcement Learning on Slow Features of High-Dimensional Input Streams.
PLoS Comput. Biol., 2010

Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons.
Neural Comput., 2010

Combining predictions for accurate recommender systems.
Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010

2009
Spiking Neurons Can Learn to Solve Information Bottleneck Problems and Extract Independent Components.
Neural Comput., 2009

Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008
A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback.
PLoS Comput. Biol., 2008

On the Classification Capability of Sign-Constrained Perceptrons.
Neural Comput., 2008

On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007
Edge of chaos and prediction of computational performance for neural circuit models.
Neural Networks, 2007

Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2005
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Comput., 2005

Wire length as a circuit complexity measure.
J. Comput. Syst. Sci., 2005

A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

2004
Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

At the Edge of Chaos: Real-time Computations and Self-Organized Criticality in Recurrent Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2002
Neural circuits for pattern recognition with small total wire length.
Theor. Comput. Sci., 2002

A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models.
Proceedings of the Biologically Motivated Computer Vision Second International Workshop, 2002

2001
On the Complexity of Knock-knee Channel-Routing with 3-Terminal Nets
Electron. Colloquium Comput. Complex., 2001

Total Wire Length as a Salient Circuit Complexity Measure for Sensory Processing
Electron. Colloquium Comput. Complex., 2001

Optimizing the Layout of a Balanced Tree
Electron. Colloquium Comput. Complex., 2001

2000
Foundations for a Circuit Complexity Theory of Sensory Processing.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000


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