Thomas Pfeil
Thomas Pfeil
Bosch Center for Artificial Intelligence
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
Deep learning with spiking neurons: Opportunities & challenges
M Pfeiffer, T Pfeil
Frontiers in Neuroscience 12, 774, 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
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
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
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
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
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
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
Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks
A Kugele, T Pfeil, M Pfeiffer, E Chicca
Frontiers in Neuroscience 14, 2020
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
Exploring the potential of brain-inspired computing
T Pfeil
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
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
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
Method, device and computer program for creating a pulsed neural network
T Pfeil, A Kugele
US Patent App. 16/937,353, 2021
ItNet: iterative neural networks with small graphs for accurate and efficient anytime prediction
T Pfeil
arXiv preprint arXiv:2101.08685, 2021
ItNet: iterative neural networks with tiny graphs for accurate and efficient anytime prediction
T Pfeil
arXiv preprint arXiv:2101.08685, 2021
ItNet: iterative neural networks for fast and efficient anytime prediction
T Pfeil
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
A spiking classifier for nonlinear problems implemented on a neuromorphic hardware system
M Schmuker, S Schrader, T Pfeil, MP Nawrot
Frontiers in Computational Neuroscience, 183, 2012
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