T. Bohnstingl

2 . 1 Learning-to-Learn and Gradient-free Optimization

W. Maass, K. Meier, T. Bohnstingl, 2019 .

Biologically-inspired training of spiking recurrent neural networks with neuromorphic hardware

A. Pantazi, G. Indiveri, T. Bohnstingl, 2022, 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS).

Towards efficient end-to-end speech recognition with biologically-inspired neural networks pdf

George Saon, Evangelos Eleftheriou, Ayush Garg, 2021, ArXiv.

Deep learning incorporating biologically inspired neural dynamics and in-memory computing

E. Eleftheriou, Stanisław Woźniak, A. Pantazi, 2020, Nature Machine Intelligence.

Deep learning incorporating biologically inspired neural dynamics and in-memory computing

E. Eleftheriou, Stanisław Woźniak, A. Pantazi, 2020 .

Deep learning incorporating biologically-inspired neural dynamics

Evangelos S. Eleftheriou, Thomas Bohnstingl, Angeliki Pantazi, 2018 .

Neuromorphic Hardware Learns to Learn

Karlheinz Meier, Wolfgang Maass, Franz Scherr, 2019, Front. Neurosci..

Online spatio-temporal learning in deep neural networks

W. Maass, E. Eleftheriou, A. Pantazi, 2020, ArXiv.

Accelerating Spiking Neural Networks using Memristive Crossbar Arrays

Angeliki Pantazi, Thomas Bohnstingl, Evangelos Eleftheriou, 2020, 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS).

Mushroom-Type phase change memory with projection liner: An array-level demonstration of conductance drift and noise mitigation

S. Ambrogio, A. Sebastian, T. Yamashita, 2021, 2021 IEEE International Reliability Physics Symposium (IRPS).

Deep learning incorporating biologically inspired neural dynamics and in-memory computing

Evangelos Eleftheriou, Thomas Bohnstingl, Angeliki Pantazi, 2020, Nature Machine Intelligence.