Franz Scherr
Title
Cited by
Cited by
Year
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets
G Bellec, F Scherr, E Hajek, D Salaj, R Legenstein, W Maass
arXiv preprint arXiv:1901.09049, 2019
262019
Neuromorphic hardware learns to learn
T Bohnstingl, F Scherr, C Pehle, K Meier, W Maass
Frontiers in neuroscience 13, 483, 2019
102019
A solution to the learning dilemma for recurrent networks of spiking neurons. bioRxiv
G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ...
00000, 738385, 2019
72019
A solution to the learning dilemma for recurrent networks of spiking neurons
G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ...
bioRxiv, 738385, 2020
62020
Reservoirs learn to learn
A Subramoney, F Scherr, W Maass
arXiv preprint arXiv:1909.07486, 2019
22019
One-shot learning with spiking neural networks
F Scherr, C Stöckl, W Maass
bioRxiv, 2020
2020
Eligibility traces provide a data-inspired alternative to backpropagation through time
G Bellec, F Scherr, E Hajek, D Salaj, A Subramoney, R Legenstein, ...
2019
Automated Security Proofs for Symmetric Ciphers
F Scherr
2015
Supplements for “One-shot learning with spiking neural networks”
F Scherr, C Stöckl, W Maass
A solution to the learning dilemma for recurrent
G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ...
Gradient-based optimization of AMEA parameters
F Scherr
Supplementary materials for: A solution to the learning dilemma for recurrent networks of spiking neurons
G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ...
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural networks
G Bellec, F Scherr, D Salaj, E Hajek, R Legenstein, W Maass
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Articles 1–13