Robert Legenstein
Robert Legenstein connects neuroscience and AI. His joint research studies learning in spiking neural networks and adaptation on brain-inspired computing hardware. Legenstein earned a computer-science doctorate at TU Graz. He studies computation and learning in biological and artificial neural systems. Legenstein coauthored Long Short-term Memory and Learning-to-learn in Networks of Spiking Neurons. The team studied adapting neurons and training methods that improve memory and transfer in recurrent spiking models. Legenstein was among ten coauthors of a preprint combining learning-to-learn with phase-change-memory hardware. The abstract reports tests on image classification and motor commands for a robotic arm, within the tested models and hardware.
- Works
- 142
- Citations
- 5,938
- h-index
- 32
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
-
2018
Memory in spiking networks
Legenstein coauthored Long Short-term Memory and Learning-to-learn in Networks of Spiking Neurons. The team studied adapting neurons and training methods that improve memory and transfer in recurrent spiking models.
-
2024 preprint
Adapting on neuromorphic hardware
Legenstein was among ten coauthors of a preprint combining learning-to-learn with phase-change-memory hardware. The abstract reports tests on image classification and motor commands for a robotic arm, within the tested models and hardware.
-
As of October 2026
Computing and neural systems
Legenstein earned a computer-science doctorate at TU Graz. He studies computation and learning in biological and artificial neural systems.
Sources and credits
Photo: neuronline.sfn.org; creator not stated. License: Not stated by source. Source: neuronline.sfn.org. Cropped to a square and resized. Ask us to remove or replace this photo.