Franz Scherr

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Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
Darjan Salajinovex GmbHVerified email at inovex.de
Guillaume BELLECEPFLVerified email at epfl.ch
Anand SubramoneyRoyal Holloway, University of LondonVerified email at rhul.ac.uk
Robert LegensteinInstitute for Theoretical Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
Prof. Claudia ClopathBioengineering, Imperial College LondonVerified email at imperial.ac.uk
Sander BohteCWI AmsterdamVerified email at cwi.nl
Iulia M. ComșaGoogle DeepMindVerified email at google.com
Richard NaudDepartment of Cellular and Molecular Medicine, Department of Physics, University of OttawaVerified email at uottawa.ca
Mihai A. PetroviciGroup Leader, NeuroTMA Lab, University of BernVerified email at unibe.ch
Dan F M GoodmanImperial College LondonVerified email at imperial.ac.uk
Karlheinz MeierRuprecht-Karls-Universität HeidelbergVerified email at kip.uni-heidelberg.de
Thomas Ortner (Bohnstingl)Post-Doctoral Researcher, Emerging Computing and Circuits, IBM Research EuropeVerified email at student.tugraz.at
Christian PehlePostDoc, Heidelberg UniversityVerified email at kip.uni-heidelberg.de
Rui Ponte CostaGroup Leader & Senior Lecturer, University of Bristol, UKVerified email at bristol.ac.uk
Arna GhoshMcGill UniversityVerified email at mail.mcgill.ca
Blake RichardsMila + McGill UniversityVerified email at mila.quebec
Luke Yuri PrinceGraphcoreVerified email at graphcore.ai
Christoph StöcklGraz University of TechnologyVerified email at tugraz.at
Timoleon MoraitisGroup Leader, Huawei Technologies - Zurich Research CenterVerified email at huawei.com

Franz Scherr

Huawei Technologies - Zurich Research Center

Verified email at huawei.com - Homepage

Machine Learning Artificial Intelligence Computational Neuroscience


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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	296*	2020
2022 roadmap on neuromorphic computing and engineering DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ... Neuromorphic Computing and Engineering 2 (2), 022501, 2022	138	2022
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	93	2019
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	48	2021
Neuromorphic hardware learns to learn T Bohnstingl, F Scherr, C Pehle, K Meier, W Maass Frontiers in neuroscience 13, 483, 2019	46	2019
Reservoirs learn to learn A Subramoney, F Scherr, W Maass Reservoir Computing: Theory, Physical Implementations, and Applications, 59-76, 2021	13	2021
One-shot learning with spiking neural networks F Scherr, C Stöckl, W Maass BioRxiv, 2020.06. 17.156513, 2020	13	2020
Neuromorphic Comput DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ... Eng 2 (1), 0-31, 2022	10	2022
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	8	2019
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	5	2022
Self-supervised learning through efference copies F Scherr, Q Guo, T Moraitis Advances in Neural Information Processing Systems 35, 2022	4	2022
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	3	2021
CCN GAC Workshop: Issues with learning in biological recurrent neural networks LY Prince, E Boven, RH Eyono, A Ghosh, J Pemberton, F Scherr, ...	3*	2021
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	1	2022
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	1	2021
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	1	2021
Dimensionality and flexibility of learning in biological recurrent neural networks BA Richards, C Clopath, RP Costa, W Maass, LY Prince, A Ghosh, ...	1	2020
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	1	2019
Data-based large-scale models provide a window into the organization of cortical computations G Chen, F Scherr, W Maass bioRxiv, 2023.04. 28.538662, 2023		2023
Competition between bottom-up visual input and internal inhibition generates error neurons in a model of the mouse primary visual cortex CR Mirasso, JG Fraile, F Scherr, JJ Ramasco, A Arkhipov, W Maass bioRxiv, 2023.01. 27.525984, 2023		2023

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