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Franz Scherr
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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 (3625), 2020
148*2020
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
Neuromorphic hardware learns to learn
T Bohnstingl, F Scherr, C Pehle, K Meier, W Maass
Frontiers in neuroscience 13, 483, 2019
322019
2022 roadmap on neuromorphic computing and engineering
DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ...
Neuromorphic Computing and Engineering, 2022
252022
Visualizing a joint future of neuroscience and neuromorphic engineering
F Zenke, SM Bohté, C Clopath, IM Comşa, J Göltz, W Maass, ...
Neuron 109 (4), 571-575, 2021
202021
Reservoirs learn to learn
A Subramoney, F Scherr, W Maass
Reservoir Computing, 59-76, 2021
102021
Roadmap on Neuromorphic Computing and Engineering
DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ML Gallo, ...
arXiv preprint arXiv:2105.05956, 2021
72021
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
One-shot learning with spiking neural networks
F Scherr, C Stöckl, W Maass
BioRxiv, 2020
42020
CCN GAC Workshop: Issues with learning in biological recurrent neural networks
LY Prince, E Boven, RH Eyono, A Ghosh, J Pemberton, F Scherr, ...
arXiv preprint arXiv:2105.05382, 2021
12021
Analysis of the computational strategy of a detailed laminar cortical microcircuit model for solving the image-change-detection task
F Scherr, W Maass
bioRxiv, 2021
12021
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
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
RH Eyono, E Boven, A Ghosh, J Pemberton, F Scherr, C Clopath, ...
Neurons, Behavior, Data analysis, and Theory, 35302, 2022
2022
Role of feature selectivity in visual perturbation responses
J Galván Fraile, F Scherr, W Maass, JJ Ramasco, CR Mirasso
2022
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
LY Prince, RH Eyono, E Boven, A Ghosh, J Pemberton, F Scherr, ...
arXiv preprint arXiv:2105.05382, 2021
2021
Is it me or is the world moving around me?
J Galván Fraile, F Scherr, W Maass, JJ Ramasco, CR Mirasso
2021
Bestärkendes Lernen und Metalernen in neuronalen Netzwerken
F Scherr
Ausgezeichnete Informatikdissertationen 2020, 2021
2021
Analysis of visual processing capabilities and neural coding strategies of a detailed model for laminar cortical microcircuits in mouse V1
G Chen, F Scherr, W Maass
bioRxiv, 2021
2021
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Articles 1–20