Michael Pfeiffer

Position:	Group Leader -- ended Oct 2016
Email:	pfeiffer@ini.uzh.ch
Work phone:	+41 44 635 30 45
Home page:	http://www.ini.uzh.ch/~pfeiffer

I studied mathematics and computer science at TU Graz in Austria, where I also obtained my PhD. Initially working on machine learning and artificial intelligence, I became more and more fascinated by the "real" intelligence in our brains, and how biology still outperforms modern computing technology in most real-world scenarios. Currently my work focuses on algorithmic approaches for understanding computation and learning in the brain, and linking observations from biology to mathematical formalisms such as probabilistic inference. I evaluate these theories and event-based algorithms in neuromorphic engineering applications, in particular for event-based vision. My goal is to advance the current state-of-the-art in artificial systems in terms of efficiency and performance, by applying the principles we learn from neuroscience. At INI, I am also coordinating the "Neural Systems and Computation" MSc program.

Main Projects

Event-based Learning and Computation

Publications

2018

Nikola I. Nikolov, Michael Pfeiffer, Richard H.R. Hahnloser Data-driven Summarization of Scientific Articles, Proc. of the 7th International Workshop on Mining Scientific Publications, LREC 2018, 2018

2017

Neil, D. and Pfeiffer, M. and Liu, S-C Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences, Advances in Neural Information Processing Systems 29 (NIPS 2016), 2017
Rueckauer, B., Lungu, I., Hu, Y. , Pfeiffer, M., and Liu, S-C. Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification, Frontiers in Neuroscience, 11: 1-12, 2017 SNN_Conversion_Frontiers

2016

Binas, J. and Indiveri, G. and and Pfeiffer, M. Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers, International Symposium on Circuits and Systems (ISCAS) 2094-2097, 2016 pdf
Binas J., Neil D., Indiveri G., Liu SC., and Pfeiffer M. Precise deep neural network computation on imprecise low-power analog hardware, arXiv, 1606.07786:, 2016
Fermüller C., Wang F., Yang Y., Zampogiannis K., Zhang Y., Barranco F., and Pfeiffer M. Prediction of Manipulation Actions, arXiv, 1610.00759:, 2016
Hu Y., Liu H., Pfeiffer M., and Delbruck T. DVS Benchmark Datasets for Object Tracking, Action Recognition and Object Recognition, Frontiers in Neuromorphic Engineering, 10:(405), 2016
J. Binas, G. Indiveri, M. Pfeiffer Deep counter networks for asynchronous event-based processing, Arxiv.org, 2016
Lee JH, Delbruck T., and Pfeiffer M. Training deep spiking neural networks using backpropagation, Frontiers in Neuromorphic Engineering, 10:(508), 2016
Neil, D., Pfeiffer, M., Liu, S.-C. Learning to be Efficient: Algorithms for Training Low-Latency, Low-Compute Deep Spiking Neural Networks, ACM Symposium on Applied Computing, 2016 pdf
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 1-24, 2016
Sumislawska D, Qiao N, Pfeiffer M, and Indiveri G Wide dynamic range weights and biologically realistic synaptic dynamics for spike-based learning circuits, International Symposium on Circuits and Systems (ISCAS) 2491-2494, 2016

2015

Binas, J. and Indiveri, G. and Pfeiffer, M. Local Structure Helps Learning Optimized Automata in Recurrent Neural Networks, IEEE International Joint Conference on Neural Networks (IJCNN), 2015 pdf
Diehl, P.U. and Neil, D. and Binas, J. and Cook, M. and Liu, S.C. and Pfeiffer, M. Fast-Classifying, High-Accuracy Spiking Deep Networks Through Weight and Threshold Balancing, IEEE International Joint Conference on Neural Networks (IJCNN), 2015 pdf
Kainz, P. and Pfeiffer, M. and Urschler, M. Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation, arXiv, 1511.06919:, 2015
Lagorce, X. and Ieng, S.H. and Clady, X. and Pfeiffer, M. and Benosman, R. Spatiotemporal Features for Asynchronous Event-based Data, Frontiers in Neuromorphic Engineering, 9:(46), 2015
Pfeiffer, M. and Betizeau, M. and Waltispurger, J. and Pfister, S. and Douglas, R.J. and Kennedy, H. and Dehay, C. Unsupervised lineage-based characterization of primate precursors reveals high proliferative and morphological diversity in the OSVZ, Journal of Comparative Neurology, 2015
Stromatias, E. and Neil, D. and Galluppi, F. and Pfeiffer, M. and Liu, S-C and Furber, S. Live Demonstration: Handwritten digit recognition using spiking Deep Belief Networks on SpiNNaker, IEEE Int. Symposium on Circuits and Systems (ISCAS), 2015
Stromatias, E. and Neil, D. and Galluppi, F. and Pfeiffer, M. and Liu, SC and Furber, S. Scalable Energy-Efficient, Low-Latency Implementations of Spiking Deep Belief Networks on SpiNNaker, IEEE International Joint Conference on Neural Networks (IJCNN), 2015 pdf
Stromatias, E. and Neil, D. and Pfeiffer, M. and Galluppi, F. and Furber, S. and Liu, S-C Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms , Frontiers in Neuromorphic Engineering, 9:(222), 2015

2014

Bauer, R. and Zubler, F. and Pfister, S. and Hauri, A. and Pfeiffer, M. and Muir, D. and Douglas, RJ. Developmental Self-construction and -Configuration of Functional Neocortical Neuronal Networks, PLoS Computational Biology, 10:(12) e1003994, 2014
Binas, J. and Rutishauser, U. and Indiveri, G. and Pfeiffer, M. Learning and Stabilization of Winner-Take-All Dynamics Through Interacting Excitatory and Inhibitory Plasticity, Frontiers in Computational Neuroscience, 8:(68), 2014
Galluppi, F. and Lagorce, X. and Stromatias, E. and Pfeiffer, M. and Plana, L.A. and Furber, S.B. and Benosman, R.B. A framework for plasticity implementation on the SpiNNaker neural architecture, Frontiers in Neuromorphic Engineering, 8:(429), 2014
Lee, J-H and Delbruck, T. and Pfeiffer, M. and Park, P.K.J. and Shin, C-W and Ryu, H. and Kang, B.C. Real-Time Gesture Interface Based on Event-Driven Processing from Stereo Silicon Retinas, IEEE Transactions on Neural Networks and Learning Systems 1-14, 2014

2013

Nessler, B. and Pfeiffer, M. and Büsing, L. and Maass, W. Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity , PLoS Computational Biology, 9:(4) e1003037, 2013
O'Connor, P. and Neil, D. and Liu, S-C. and Delbruck, T. and Pfeiffer, M. Real-Time Classification and Sensor Fusion with a Spiking Deep Belief Network, Frontiers in Neuromorphic Engineering, 7:(178), 2013
Sheik, S and Pfeiffer, M and Stefanini, F and Indiveri, G Spatio-temporal Spike Pattern Classification in Neuromorphic Systems, Biomimetic and Biohybrid Systems 262-273, 2013

2012

Lee, J. and Delbruck, T. and Park, P.K.J. and Pfeiffer, M. and Shin, C.W. and Ryu, H. and Kang, B.C. Live demonstration: Gesture-Based remote control using stereo pair of dynamic vision sensors, IEEE Intl. Symp. on Circuits and Systems (ISCAS 2012) 736-740, 2012

2011

Römer, H., and Lang, A. and Pfeiffer, M. and Hartbauer, M. A cost-benefit analysis of public and private communication, Communicative & Integrative Biology, 4:(1) 106-108, 2011

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