Thomas Pfeil

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Johannes SchemmelHeidelberg UniversityAdresse e-mail validée de kip.uni-heidelberg.de
Karlheinz MeierRuprecht-Karls-Universität HeidelbergAdresse e-mail validée de kip.uni-heidelberg.de
Michael PfeifferBosch Center for Artificial IntelligenceAdresse e-mail validée de de.bosch.com
Michael SchmukerBiocomputation Group, University of Hertfordshire, UKAdresse e-mail validée de biomachinelearning.net
Andreas GrüblHeidelberg UniversityAdresse e-mail validée de kip.uni-heidelberg.de
Mihai A. PetroviciGroup Leader, NeuroTMA Lab, University of Bern / Heidelberg UniversityAdresse e-mail validée de unibe.ch
Martin Paul NawrotProfessor for Computational Systems Neuroscience, University of CologneAdresse e-mail validée de uni-koeln.de
Markus DiesmannDirector, INM-6 and IAS-6, Jülich Research CentreAdresse e-mail validée de fz-juelich.de
Alexander KugelePhD student, Bosch Center for Artificial Intelligence and University of GroningenAdresse e-mail validée de rug.nl
Tom TetzlaffJülich Research Centre, GermanyAdresse e-mail validée de fz-juelich.de
Elisabetta ChiccaZernike Institute for Advanced Materials - University of GroningenAdresse e-mail validée de rug.nl
Jakob JordanDepartment of Physiology, University of BernAdresse e-mail validée de unibe.ch
Volker FischerBosch Center for Artificial IntelligenceAdresse e-mail validée de de.bosch.com
Oliver BreitwieserDr. rer. nat., Heidelberg UniversityAdresse e-mail validée de kip.uni-heidelberg.de
Eric MüllerRuprecht-Karls-Universität HeidelbergAdresse e-mail validée de kip.uni-heidelberg.de
Jan Mathias KöhlerEnable AI / Bosch Center for Artificial IntelligenceAdresse e-mail validée de enable-ai.de

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Thomas Pfeil

Bosch Center for Artificial Intelligence

Adresse e-mail validée de mailbox.org

Deep Learning Neuromorphic Engineering Brain-inspired Computing


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Deep learning with spiking neurons: Opportunities & challenges M Pfeiffer, T Pfeil Frontiers in Neuroscience 12, 774, 2018	377	2018
Six networks on a universal neuromorphic computing substrate T Pfeil, A Grübl, S Jeltsch, E Müller, P Müller, MA Petrovici, M Schmuker, ... Frontiers in neuroscience 7, 2013	201	2013
A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems D Brüderle, MA Petrovici, B Vogginger, M Ehrlich, T Pfeil, S Millner, ... Biological cybernetics 104 (4), 263-296, 2011	134	2011
Is a 4-bit synaptic weight resolution enough?–constraints on enabling spike-timing dependent plasticity in neuromorphic hardware T Pfeil, TC Potjans, S Schrader, W Potjans, J Schemmel, M Diesmann, ... Frontiers in Neuroscience 6, 2012	110	2012
A neuromorphic network for generic multivariate data classification M Schmuker, T Pfeil, MP Nawrot Proceedings of the National Academy of Sciences 111 (6), 2081-2086, 2014	98	2014
Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks A Kugele, T Pfeil, M Pfeiffer, E Chicca Frontiers in Neuroscience 14, 2020	39	2020
Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study T Pfeil, J Jordan, T Tetzlaff, A Grübl, J Schemmel, M Diesmann, K Meier Physical Review X 6 (2), 021023, 2016	30	2016
Pattern representation and recognition with accelerated analog neuromorphic systems MA Petrovici, S Schmitt, J Klähn, D Stöckel, A Schroeder, G Bellec, J Bill, ... 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 1-4, 2017	18	2017
Neuromorphic learning towards nano second precision T Pfeil, AC Scherzer, J Schemmel, K Meier The 2013 International Joint Conference on Neural Networks (IJCNN), 1-5, 2013	16	2013
The streaming rollout of deep networks-towards fully model-parallel execution V Fischer, J Köhler, T Pfeil Advances in Neural Information Processing Systems, 4039-4050, 2018	13	2018
Exploring the potential of brain-inspired computing T Pfeil	7	2015
Configuration strategies for neurons and synaptic learning in large-scale neuromorphic hardware systems T Pfeil Diploma thesis (English), University of Heidelberg, HD-KIP 11-34, 2011	6	2011
Fast sampling with neuromorphic hardware MA Petrovici, D Stöckel, I Bytschok, J Bill, T Pfeil, J Schemmel, K Meier arXiv preprint arXiv:1311.3211, 2013	4	2013
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision A Kugele, T Pfeil, M Pfeiffer, E Chicca DAGM German Conference on Pattern Recognition, 297-312, 2021	2	2021
Neural Networks as Sources of uncorrelated Noise for functional neuralSystems J Jordan, I Bytschok, T Tetzlaff, T Pfeil, O Breitwieser, J Bill, M Diesmann, ... INM Retreat 2014, 2014	2	2014
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning M Nonnenmacher, T Pfeil, I Steinwart, D Reeb International Conference on Learning Representations, 2021	1	2021
Classification of multivariate data with a spiking neural network on neuromorphic hardware M Schmuker, T Pfeil, MP Nawrot BMC Neuroscience 14 (1), 1-1, 2013	1	2013
DEVICE AND METHOD FOR TRAINING A CLASSIFIER USING AN INVERTIBLE FACTORIZATION MODEL V Fischer, CK Mummadi, T Pfeil US Patent App. 17/448,110, 2022		2022
DEVICE AND METHOD FOR CLASSIFYING AN INPUT SIGNAL USING AN INVERTIBLE FACTORIZATION MODEL V Fischer, CK Mummadi, T Pfeil US Patent App. 17/448,144, 2022		2022
More flexible iterative operation of artificial neural networks T Pfeil US Patent App. 17/394,780, 2022		2022

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