Michael Pfeiffer, PhD

Affiliations: 
Institute of Neuroinformatics ETH/Uni Zurich, Zürich, Zürich, Switzerland 
Area:
Machine Learning, Computational Neuroscience
Google:
"Michael Pfeiffer"
Mean distance: 14.58 (cluster 17)
 
SNBCP
Cross-listing: Computational Biology Tree

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Publications

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Kassraian-Fard P, Pfeiffer M, Bauer R. (2020) A generative growth model for thalamocortical axonal branching in primary visual cortex. Plos Computational Biology. 16: e1007315
Stromatias E, Neil D, Pfeiffer M, et al. (2015) Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms. Frontiers in Neuroscience. 9: 222
Pfeiffer M, Betizeau M, Waltispurger J, et al. (2015) Unsupervised lineage-based characterization of primate precursors reveals high proliferative and morphological diversity in the OSVZ. The Journal of Comparative Neurology
Lagorce X, Ieng SH, Clady X, et al. (2015) Spatiotemporal features for asynchronous event-based data. Frontiers in Neuroscience. 9: 46
Delbruck T, Pfeiffer M, Juston R, et al. (2015) Human vs. computer slot car racing using an event and frame-based DAVIS vision sensor Proceedings - Ieee International Symposium On Circuits and Systems. 2015: 2409-2412
Stromatias E, Neil D, Galluppi F, et al. (2015) Live demonstration: Handwritten digit recognition using spiking deep belief networks on SpiNNaker Proceedings - Ieee International Symposium On Circuits and Systems. 2015: 1901
Binas J, Indiveri G, Pfeiffer M. (2015) Local structure helps learning optimized automata in recurrent neural networks Proceedings of the International Joint Conference On Neural Networks. 2015
Diehl PU, Neil D, Binas J, et al. (2015) Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing Proceedings of the International Joint Conference On Neural Networks. 2015
Stromatias E, Neil D, Galluppi F, et al. (2015) Scalable energy-efficient, low-latency implementations of trained spiking Deep Belief Networks on SpiNNaker Proceedings of the International Joint Conference On Neural Networks. 2015
Pfeiffer M. (2015) Determination of realistic sterilisation conditions for products contaminated with a bioburden of various species / Calculation of realistic sterilisation doses based on experimentally determined D-values, e. g., sterilisation with ionising irradiation | Ermittlung realitätsnaher Sterilisationsbedingungen: Produkte, die mit mehr als nur einer Keimart verkeimt sind / Berechnung ausreichender und realistischer Sterilisationsdosen anhand von ermittelten D-Werten / am Beispiel der Sterilisation mit ionisierenden Strahlen Pharmazeutische Industrie. 77: 1082-1086
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