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Franz Scherr
Franz Scherr
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Jahr
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
1722020
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
752019
2022 roadmap on neuromorphic computing and engineering
DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ...
Neuromorphic Computing and Engineering 2 (2), 022501, 2022
412022
Neuromorphic hardware learns to learn
T Bohnstingl, F Scherr, C Pehle, K Meier, W Maass
Frontiers in neuroscience 13, 483, 2019
332019
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
232021
Reservoirs learn to learn
A Subramoney, F Scherr, W Maass
Reservoir Computing, 59-76, 2021
112021
A solution to the learning dilemma for recurrent networks of spiking neurons Nat
G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ...
Commun 11 (1), 1, 2020
82020
One-shot learning with spiking neural networks
F Scherr, C Stöckl, W Maass
BioRxiv, 2020
72020
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
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
22021
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
22021
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
12021
Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks
A Subramoney, G Bellec, F Scherr, R Legenstein, W Maass
bioRxiv, 2021
12021
Dimensionality and flexibility of learning in biological recurrent neural networks
BA Richards, C Clopath, RP Costa, W Maass, LY Prince, A Ghosh, ...
12020
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
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