Matthew Cook

Group Leader
Work phone:
41 44 635 3097
Home page:
Cortical Computation

How does thinking work? How does the cortex compute? This is one of today's greatest mysteries in science. We do not yet know how to make machines do computations similar to the computations done with ease by animal brains. By experimenting with cortically inspired architectures, we hope to gain an understanding of how such computation can occur. One of our current directions is examining models similar to belief propagation on factor graphs, and how such models can be adapted to naturally solve learning and control problems of the sort that brains solve naturally.


INI-401, 227-1037-00 Introduction to Neuroinformatics
INI-427, 252-1424-00 Models of Computation
INI-431, 227-1045-00 Readings in Neuroinformatics
INI-434, 227-1049-00 Block: Insights Into Neuroinformatics




  • Diehl, P.U. and Cook, M. Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity, Transactions of Neural Networks and Learning Systems(submitted), 2014 code | pdf
  • Diehl, P.U. and Cook, M. Efficient Implementation of STDP Rules on SpiNNaker Neuromorphic Hardware, IEEE International Joint Conference on Neural Networks (IJCNN), 2014 pdf
  • Funke, Jan and Martel, Julien and Gerhard, Stephan and Ciresan, Björn Andres Dan C. and Giusti, Alessandro and Gambardella, Luca and Pfister, Jürgen Schmidhuber Hanspeter and Cardona, Albert and Cook, Matthew Candidate Sampling for Neuron Reconstruction from Anisotropic Electron Microscopy Volumes, MICCAI 2014, Medical Image Computing and Computer Assisted Intervention, (proceedings are in Lecture Notes in Computer Science), 8673: 17-24, 2014
  • Muir, D. and Cook, M. Anatomical Constraints on Lateral Competition in Columnar Cortical Architectures, Neural Computation, 26:(8) 1624-1666, 2014


  • Corneil, D. and Sonnleithner, D. and Neftci, E. and Chicca, E. and Cook, M. and Indiveri, G. and Douglas, R. Real-time inference in a VLSI spiking neural network, IEEE International Symposium on Circuits and Systems (ISCAS) 2425-2428, 2012
  • Corneil, D. and Sonnleithner, D. and Neftci, E. and Chicca, E. and Cook, M. and Indiveri, G. and Douglas, R. Function approximation with uncertainty propagation in a VLSI spiking neural network, International Joint Conference on Neural Networks (IJCNN) 1-7, 2012
  • Funke, Jan and Andres, Bjoern and Hamprecht, Fred A. and Cardona, Albert and Cook, Matthew Efficient Automatic 3D-Reconstruction of Branching Neurons from EM Data , Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2012 1004 - 1011, 2012



  • Neftci, Emre and Chicca, Elisabetta and Cook, Matthew and Douglas, Rodney State-Dependent Sensory Processing in Networks of VLSI Spiking Neurons, Iscas proceedings 2010 2789 - 2792, 2010 pdf


  • Conradt, J. and Berner, R. and Cook, M. and Delbruck, T. An Embedded AER Dynamic Vision Sensor for Low-Latency Pole Balancing, IEEE Workshop on Embedded Computer Vision (ECV09), Kyoto, Japan, 2009 pdf
  • Conradt, J and Cook, M and Berner, R and Lichtsteiner, P and Douglas, RJ and Delbruck , T A Pencil Balancing Robot using a Pair of AER Dynamic Vision Sensors, International Conference on Circuits and Systems (ISCAS) 781-785, 2009 pdf
  • Cook, M and Jug, F and Krautz, C Sharpening Projections, BMC Neuroscience, 10: (Suppl 1):P214, 2009 pdf
  • Cook, Matthew and Soloveichik, David and Winfree, Erik and Bruck, Jehoshua Programmability of Chemical Reaction Networks, Algorithmic Bioprocesses 543-584, 2009 pdf



  • Jiang, A. and Cook, M. and Bruck, J. Optimal Interleaving on Tori, SIAM Journal on Discrete Mathematics, 20:(4) 841-879, 2006 pdf
© 2015 Institut für Neuroinformatik