Dr. Michael Pfeiffer

Journal articles

  • Y. Hu, H. Liu, M. Pfeiffer, T. Delbruck, DVS Benchmark Datasets for Object Tracking, Action Recognition and Object Recognition. Frontiers in Neuromorphic Engineering, 10:405, 2016.
  • M. Pfeiffer, M. Betizeau, J. Waltispurger, S. Pfister, RJ Douglas, H. Kennedy, C. Dehay, Unsupervised lineage-based characterization of primate precursors reveals high proliferative and morphological diversity in the OSVZ. Journal of Comparative Neurology, 2016.
  • E. Stromatias, D. Neil, M. Pfeiffer, F. Galluppi, S. Furber, SC Liu, Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms. Frontiers in Neuromorphic Engineering 9:222, 2015. [link]
  • X. Lagorce, SH Ieng, X. Clady, M. Pfeiffer, R. Benosman, Spatiotemporal Features for Asynchronous Event-based Data. Frontiers in Neuromorphic Engineering 9:46, 2015. [link]
  • F. Galluppi, X. Lagorce, E. Stromatias, M. Pfeiffer, L. Plana, S. Furber, R. Benosman, A framework for plasticity implementation on the SpiNNaker neural architecture. Frontiers in Neuromorphic Engineering 8:429, 2014. [link]
  • R. Bauer, F. Zubler, S. Pfister, A. Hauri, M. Pfeiffer, D.R. Muir, R.J. Douglas, Developmental self-construction and -configuration of functional neocortical neuronal networks. PLoS Computational Biology 10(12), e1003994, 2014. [link]
  • J. Binas, U. Rutishauser, G. Indiveri, M. Pfeiffer, Learning and Stabilization of Winner-Take-All Dynamics Through Interacting Excitatory and Inhibitory Plasticity. Frontiers in Computational Neuroscience 8(68), 2014. [link]
  • JH Lee, T. Delbruck, M. Pfeiffer, PKJ Park, CW Shin, H. Ryu, BC Kang, Real-Time Gesture Interface Based on Event-Driven Processing from Stereo Silicon Retinas. IEEE Transactions on Neural Networks and Learning Systems, 2014. [link]
  • P. O'Connor, D. Neil, S-C Liu, T. Delbruck, M. Pfeiffer, Real-Time Classification and Sensor Fusion with a Spiking Deep Belief Network. Frontiers in Neuromorphic Engineering 7(178), 2013. [link]
  • B. Nessler, M. Pfeiffer, L. Büsing, W. Maass, Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity. PLoS Computational Biology 9(4), e1003037, 2013. [link] [suppl. material]
  • M. Pfeiffer, M. Hartbauer, A. Lang, W. Maass, H. Römer, Probing Real Sensory Worlds of Receivers with Unsupervised Clustering. PLoS ONE 7(6), 2012. [link]
  • H. Römer, A. Lang, M. Pfeiffer, M. Hartbauer, A cost-benefit analysis of public and private communication. Communicative and Integrative Biology 4 (1), 1-2. 2011. [PDF]
  • M. Pfeiffer, B. Nessler, R. Douglas, and W. Maass, Reward-modulated Hebbian Learning of Decision Making. Neural Computation 22(6), 1399-1444. 2010. [link]
  • M. Pfeiffer: Machine Learning Applications in Computer Games, ÖGAI-Journal 23 (3). 2004.

Preprints

  • J. Binas, D. Neil, G. Indiveri, S-C Liu, M. Pfeiffer, Precise deep neural network computation on imprecise low-power analog hardware. arXiv 1606.07786. 2016. [arXiv]
  • Sirinukunwattana K., Pluim J., Chen H., Qi X., Heng P-A, Guo YB, Wang LY, Matuszewski B., Bruni E., Sanchez U., Böhm A., Ronneberger O., Cheikh BB, Racoceanu D., Kainz P., Pfeiffer M., Urschler M., Snead D., and Rajpoot N., Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest. arXiv 1603.00275, 2016. [arXiv]
  • J. Binas, G. Indiveri, M. Pfeiffer, Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers. arXiv 1511.00540. 2015. [arXiv]
  • P. Kainz, M. Pfeiffer, M. Urschler, Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation. arXiv 1511.06919. 2015. [arXiv]

Conference proceedings and demos (peer reviewed)

  • J. Binas, G. Indiveri, M. Pfeiffer, Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers. IEEE Int. Symposium on Circuits and Systems (ISCAS), Montreal, Canada. 2016. [PDF]
  • D. Sumislawska, N. Qiao, M. Pfeiffer, G. Indiveri, Wide dynamic range weights and biologically realistic synaptic dynamics for spike-based learning circuits. IEEE Int. Symposium on Circuits and Systems (ISCAS), Montreal, Canada. 2016.
  • D. Neil, M. Pfeiffer, and S-C Liu.: Learning to be efficient: Learning to be Efficient: Algorithms for Training Low-Latency, Low-Compute Deep Spiking Neural Networks. ACM Symposium on Applied Computing, Pisa, Italy. 2016. [PDF]
  • P. Diehl, D. Neil, J. Binas, M. Cook. S-C Liu, and M. Pfeiffer: Fast-Classifying, High-Accuracy Spiking Deep Networks Through Weight and Threshold Balancing. Int. Joint Conference on Neural Networks (IJCNN), Killarney, Ireland. 2015. [PDF]
  • E. Stromatias, D. Neil, F. Galluppi, M. Pfeiffer, S-C Liu, and S. Furber: Scalable Energy-Efficient, Low-Latency Implementations of Spiking Deep Belief Networks on SpiNNaker. Int. Joint Conference on Neural Networks (IJCNN), Killarney, Ireland. 2015. [PDF]
  • J. Binas, G. Indiveri, and M. Pfeiffer: Local Structure Helps Learning Optimized Automata in Recurrent Neural Networks. Int. Joint Conference on Neural Networks (IJCNN), Killarney, Ireland. 2015. [PDF]
  • E. Stromatias, D. Neil, F. Galluppi, M. Pfeiffer, S-C Liu, and S. Furber: Live Demonstration: Handwritten digit recognition using spiking Deep Belief Networks on SpiNNaker. IEEE Int. Symposium on Circuits and Systems (ISCAS), Lissabon, Portugal. 2015.
  • T. Delbruck, M. Pfeiffer, R. Juston, G. Orchard, E. Müggler, A. Linares-Barranco, and M.W. Tilden: Human vs computer slot car racing using an event and frame-based vision sensor, IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, Portugal. 2015.
  • D. Corneil, E. Neftci, G. Indiveri, M. Pfeiffer: Learning, Inference, and Replay of Hidden States in Recurrent Spiking Neural Networks. Cosyne Conference Abstract. Salt Lake City. 2014. [Link to Poster]
  • S. Sheik, M. Pfeiffer, F. Stefanini, G. Indiveri: Spatio-temporal spike pattern classification in neuromorphic systems. Proceedings of Living Machines: International Conference on Biomimetic and Biohybrid Systems. London. 2013.
  • J. Lee, T. Delbruck, P.K.J. Park, M. Pfeiffer, C.W. Shin, H. Ryu, and B.C. Kang: Gesture-Based remote control using stereo pair of dynamic vision sensors (Best live demonstration award), ISCAS, Seoul, South Korea. 2012.
  • B. Nessler, M. Pfeiffer, W. Maass: STDP enables spiking neurons to detect hidden causes of their inputs, Proceedings of NIPS 22, Vancouver, Canada. 2009.
  • B. Nessler, M. Pfeiffer, and W. Maass: Hebbian learning of Bayes optimal decision. Proceedings of NIPS 21, Vancouver, Canada. 2008.
  • G. Neumann, M. Pfeiffer, and W. Maass: Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs. Proceedings of ECML-18, Warsaw, Poland. 2007.
  • M. Pfeiffer, A. Saffari A.A., A. Juffinger: Predicting Text Relevance from Sequential Reading Behavior. Proceedings of NIPS Workshop on Implicit Feedback and User Modeling, Whistler, Canada. 2005.
  • M. Pfeiffer: Reinforcement Learning of Strategies for Settlers of Catan, Proceedings of the International Conference on Computer Games: Artificial Intelligence, Design and Education, Reading, UK. 2004.

Theses

  • M. Pfeiffer, Concepts and methods from machine learning as tools for the analysis of computations in nervous systems. PhD Thesis. 2010.
  • M. Pfeiffer: Machine Learning Applications in Computer Games. Master's Thesis, Institute for Theoretical Computer Science, Graz University of Technology. 2003.
Last changed: 23 August 2016