Follow
Darjan Salaj
Darjan Salaj
inovex GmbH
Verified email at inovex.de - Homepage
Title
Cited by
Cited by
Year
Long short-term memory and learning-to-learn in networks of spiking neurons
G Bellec, D Salaj, A Subramoney, R Legenstein, W Maass
Advances in neural information processing systems 31, 2018
2662018
A solution to the learning dilemma for recurrent networks of spiking neurons
G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ...
Nature communications 11 (1), 1-15, 2020
1372020
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
682019
Advances in neural information processing systems
G Bellec, D Salaj, A Subramoney, R Legenstein, W Maass, S Bengio, ...
Curran, Red Hook, NY 31, 787-797, 2018
242018
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
102019
A solution to the learning dilemma for recurrent networks of spiking neurons. bioRxiv, 738385
G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ...
72019
Spike-frequency adaptation provides a long short-term memory to networks of spiking neurons
D Salaj, A Subramoney, C Kraišniković, G Bellec, R Legenstein, W Maass
bioRxiv, 2020.05. 11.081513, 2020
62020
Spike frequency adaptation supports network computations on temporally dispersed information
D Salaj, A Subramoney, C Kraisnikovic, G Bellec, R Legenstein, W Maass
Elife 10, e65459, 2021
52021
Eligibility traces provide a data-inspired alternative to backpropagation through time
G Bellec, F Scherr, E Hajek, D Salaj, A Subramoney, R Legenstein, ...
52019
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets. arXiv
G Bellec, F Scherr, E Hajek, D Salaj, R Legenstein, W Maass
arXiv preprint arXiv:1901.09049, 2019
52019
Long short-term memory and learning-to-learn in networks of spiking neurons. arXiv
G Bellec, D Salaj, A Subramoney, R Legenstein, W Maass
arXiv preprint arXiv:1803.09574, 2018
52018
Slow processes of neurons enable a biologically plausible approximation to policy gradient
A Subramoney, G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, ...
33nd NeurIPS workshop, 2019
12019
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
1
on STORE-RECALL task.
D Salaj, A Subramoney, C Kraišnikovic, G Bellec, R Legenstein, W Maass
2021
Spike frequency adaptation supports network computations
D Salaj, A Subramoney, C Kraišnikovic, G Bellec, R Legenstein, W Maass
2020
Spike-frequency adaptation contributes long short-term memory to networks of spiking neurons
A Subramoney, C Kraisnikovic, D Salaj, GEF Bellec, R Legenstein, ...
2020 Bernstein Conference, 2020
2020
Spike frequency adaptation supports network computations on temporally dispersed information Open Website
D Salaj, A Subramoney, C Kraisnikovic, G Bellec, R Legenstein, W Maass
Bio-Inspired Neuromorphic AI Methods Enables Privacy Respecting Security and Surveillance
N Delilovic, D Salaj
A solution to the learning dilemma for recurrentnetworks of spiking neurons Download PDF Open Website
G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ...
Long short-term memory and learning-to-learn in networks of spiking neurons Download PDF
G Bellec, D Salaj, A Subramoney, R Legenstein, W Maass
The system can't perform the operation now. Try again later.
Articles 1–20