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Franz Scherr
Franz Scherr
Huawei Technologies - Zurich Research Center
Geverifieerd e-mailadres voor huawei.com - Homepage
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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
4492020
2022 roadmap on neuromorphic computing and engineering
DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ...
Neuromorphic Computing and Engineering 2 (2), 022501, 2022
3552022
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
1052019
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
722021
Neuromorphic hardware learns to learn
T Bohnstingl*, F Scherr*, C Pehle, K Meier, W Maass
Frontiers in neuroscience 13, 483, 2019
532019
Neuromorphic Comput
DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ...
Eng 2, 022501, 2022
352022
Reservoirs learn to learn
A Subramoney, F Scherr, W Maass
Reservoir Computing: Theory, Physical Implementations, and Applications, 59-76, 2021
222021
A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing
G Chen, F Scherr, W Maass
science advances 8 (44), eabq7592, 2022
202022
One-shot learning with spiking neural networks
F Scherr, C Stöckl, W Maass
BioRxiv, 2020.06. 17.156513, 2020
192020
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
10*2021
Eligibility traces provide a data-inspired alternative to backpropagation through time
G Bellec*, F Scherr*, E Hajek, D Salaj, A Subramoney, R Legenstein, ...
NeurIPS 2019 workshop "Real Neurons & Hidden Units: Future directions at the …, 2019
102019
Self-supervised learning through efference copies
F Scherr, Q Guo, T Moraitis
Advances in Neural Information Processing Systems 35, 2022
82022
Analysis of the computational strategy of a detailed laminar cortical microcircuit model for solving the image-change-detection task
F Scherr, W Maass
bioRxiv, 2021.11. 17.469025, 2021
42021
Competition between bottom-up visual input and internal inhibition generates error neurons in a model of the mouse primary visual cortex
JG Fraile, F Scherr, JJ Ramasco, A Arkhipov, W Maass, CR Mirasso
bioRxiv, 2023.01. 27.525984, 2023
32023
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, 2021.01. 25.428153, 2021
32021
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 1, 2022
22022
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, 2021.12. 07.471653, 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, ...
NeurIPS 2019 workshop "Biological and Artificial Reinforcement Learning", 2019
12019
Fast learning without synaptic plasticity in spiking neural networks
A Subramoney, G Bellec, F Scherr, R Legenstein, W Maass
Scientific Reports 14 (1), 8557, 2024
2024
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Artikelen 1–20