Prof. Dr. rer. nat. Thomas Martinetz

2016

• Burciu, I., Martinetz, T., and Barth, E.: Hierarchical Manifold Sensing with Foveation and Adaptive Partitioning of the Dataset: Journal of Imaging Science and Technology, vol. 60, no. 2, pp. 020402-1-020402-10, 2016
  
• Danielmeyer, H. G. and Martinetz, T.: The industrial society`s intrinsic dynamics: , 2016, Abstract
  
• Knyazew, B., Barth, E., and Martinetz, T.: Autoconvolution for Unsupervised Feature Learning: arXiv:1606.00611v1 [cs.CV], 2016
  
• Maier, O., Schröder, C., Forkert, N. D., Martinetz, T., and Handels, H.: Correction: Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study: PloS one, vol. 11, no. 2, Public Library of Science, , 2016
  
• Schellenberger Costa, M., Born, J., Claussen, J. C., and Martinetz, T.: Modeling the effect of sleep regulation on a neural mass model: Journal of Computational Neuroscience, vol. 41, no. 1, pp. 15-28, 2016
  
• Schellenberger Costa, M., Weigenand, A., Ngo, H. V., Marshall, L., Born, J., Martinetz, T., and Claussen, J. C.: A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation: PloS computational Biology, vol. 12, no. 9, , 2016
  
• Weigenand, A., Mölle, M., Werner, F., Martinetz, T., and Marshall, L.: Timing matters: open-loop stimulation does not improve overnight consolidation of word pairs in humans: European Journal of Neuroscience, vol. 44, pp. 2357-2368, 2016
  
• Weigenand, A., Schellenberger Costa, M., Ngo, H., Mölle, M., Marshall, L., Claussen, J. C., and Martinetz, T.: A thalamocortical neural mass model of evoked potentials during NREM sleep: Cosyne Abstracts 2016, Salt Lake City USA, 2016
  
• Schütze, H., Barth, E., and Martinetz, T.: Learning Efficient Data Representations with Orthogonal Sparse Coding: IEEE Transactions on Computational Imaging, vol. 2, no. 3, pp. 177-189, 09 2016
  

2015

• Burciu, I., Martinetz, T., and Barth, E.: Foveated Manifold Sensing for object recognition: IEEE International Black Sea Conference on Communications and Networking, pp. 196-200, 2015
  
• Coleca, F., State, A., Klement, S., Barth, E., and Martinetz, T.: Self-organizing maps for hand and full body tracking: Neurocomputing, vol. 147, Elsevier, pp. 174-184, 2015, Advances in Self-Organizing Maps Subtitle of the special issue: Selected Papers from the Workshop on Self-Organizing Maps 2012 (WSOM 2012)
  
• Dornseifer, S., Willkomm, S., Kretschmer-Kazemi Far, R., Liebschwager, J., Beltsiou, F., Frank, K., Laufer, S. D., Martinetz, T., Sczakiel, G., Claussen, J. C., and Restle, T.: RNAi revised-target mRNA-dependent enhancement of gene silencing: Nucleic acids research, vol. 43, no. 22, pp. 10623-10632, 2015
  
• Hocke, J. and Martinetz, T.: Learning Transformation Invariance from Global to Local: Workshop New Challenges in Neural Computation 2015, vol. 03, Hammer, B. and Villmann, T. (Ed.), pp. 16-24, 2015
  
• Hocke, J. and Martinetz, T.: Maximum Distance Minimization for Feature Weighting: Pattern Recognition Letters, vol. 52, pp. 48-52, 2015
  
• Krause, C., Ens, K., Fechner, K., Voigt, J., Fraune, J., Rohwäder, E., Hahn, M., Danckwardt, M., Feirer, C., Barth, E., Martinetz, T., and Stöcker, W.: EUROPattern Suite technology for computer-aided immunofluorescence microscopy in autoantibody diagnostics: Lupus, vol. 24, no. 4-5, pp. 516-529, 2015
  
• Maier, O., Schröder, C., Forkert, N. D., Martinetz, T., and Handels, H.: Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study: PloS one, vol. 10, no. 12, Public Library of Science, , 2015
  
• Ngo, H. V., Miedema, A., Faude, I., Martinetz, T., Mölle, M., and Born, J.: Driving Sleep Slow Oscillations by Auditory Closed-Loop Stimulation - A Self-Limiting Process: The Journal of Neuroscience, vol. 35, no. 17, pp. 6630-6638, 2015
  
• Schütze, H., Barth, E., and Martinetz, T.: Learning Orthogonal Sparse Representations by Using Geodesic Flow Optimization: IJCNN 2015 Conference Proceedings, pp. 15540:1-8, 2015
  
• Hertel, L., Barth, E., Käster, T., and Martinetz, T.: Deep Convolutional Neural Networks as Generic Feature Extractors: Proc. 2015 IEEE International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 07 2015
  

2014

• Burciu, I., Ion-Margineanu, A., Martinetz, T., and Barth, E.: Visual Manifold Sensing, booktitle Human Vision and Electronic Imaging XIX: , vol. 9014, Proceedings of SPIE Electronic Imaging, pp. 48:1-8, 2014
  
• Hammer, B., He, H., and Martinetz, T.: Learning and modelling big data: ESANN 2014 Proceedings, European Symposium on Artificial Networks, Computational Intelligence and Machine learning. Bruges (Belgium, 23-25 April 2014, pp. 343-352, 2014
  
• Hocke, J. and Martinetz, T.: Global Metric Learning by Gradient Descent: Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings, vol. 8681, Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P. D., Magg, S., Palm, G., and Villa, A. E. P. (Ed.), Springer, pp. 129-135, 2014
  
• Hocke, J. and Martinetz, T.: Learning Transformation Invariance for Object Recognition: Workshop New Challenges in Neural Computation 2014, vol. 02, Hammer, B. and Villmann, T. (Ed.), pp. 20-25, 2014
  
• Miclut, B., Käster, T., Martinetz, T., and Barth, E.: Committees of deep feedforward networks trained with few data: arXiv:1406.5947v1[cs.CV], 2014
  
• Schütze, H., Barth, E., and Martinetz, T.: An adaptive hierarchical sensing scheme for sparse signals: Human Vision and Electronic Imaging XIX, vol. 9014, Rogowitz, B. E., Pappas, T. N., and Ridder, H. d. (Ed.), pp. 15:1-8, 2014
  
• Weigenand, A., Schellenberger Costa, M., Ngo, H. V., Claussen, J. C., and Martinetz, T.: Characterization of K-Complexes and Slow Wave Activity in a Neural Mass Model: PLoS Computational Biology, vol. 10, , 2014
  

2013

• Coman, H., Barth, E., and Martinetz, T.: Sparse Coding Neural Gas Applied to Image Recognition: Advances in Self-Organizing Maps, vol. 198, Springer Berlin Heidelberg, pp. 105-114, 2013
  
• Coleca, F., Klement, S., Martinetz, T., and Barth, E.: Real-time skeleton tracking for embedded systems: Mobile Computational Photography, vol. 8667D, Proceedings of SPIE, 2013
  
• Coleca, F., Martinetz, T., and Barth, E.: Gesture Interfaces with Depth Sensors: Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications, vol. 8200, Springer Berlin Heidelberg, pp. 207-227, 2013
  
• Hochstrate, N., Krause, C., Ens, K., Danckwardt, M., Feirer, C., Affeldt, K., Rateike, M., Morrin, M., Wuttig, D., Voigt, J., Fechner, K., Barth, E., Martinetz, T., and Stöcker, W.: Computer-aided immunofluorescence microscopy (CAIFM) for ANCA diagnostics: La Presse Medicale, vol. 42, no. 4, Elsevier Masson, , 2013
  
• Hochstrate, N., Krause, C., Ens, K., Danckwardt, M., Feirer, C., Rohwäder, E., Hahn, M., Affeldt, K., Rateike, M., Morrin, M., Wuttig, D., Fechner, K., Barth, E., Stöcker, W., Martinetz, T., and Voigt, J.: ANA and ANCA diagnostics with computer-aided immunofluorescence microscopy (CAIFM): Zeitschrift für Rheumatologie, vol. 72, Springer Heidelberg, , 2013
  
• Hocke, J. and Martinetz, T.: Feature Weighting by Maximum Distance Minimization: Artificial Neural Networks and Machine Learning - ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 10-13, 2013. Proceedings, vol. 8131, Mladenov, V., Koprinkova-Hristova, P. D., Palm, G., Villa, A. E. P., Appollini, B., and Kasabov, N. (Ed.), Springer, pp. 420-425, 2013
  
• Hocke, J. and Martinetz, T.: Application of Maximum Distance Minimization to Gene Expression Data: Workshop New Challenges in Neural Computation 2013, vol. 02, Hammer, B., Martinetz, T., and Villmann, T. (Ed.), pp. 6-7, 2013, Short Paper
  
• Kandaswamy, K. K., Pugalenthi, G., Kalies, K., Hartmann, E., and Martinetz, T.: EcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection: Journal of Theoretical Biology, vol. 317, pp. 377-383, 2013
  
• Klement, S., Anders, S., and Martinetz, T.: The Support Vector Machine: Classification with the Least Number of Features and Application to Neuroimaging Data: Neural Computation, vol. 25/6, pp. 1548-1584, 2013
  
• Ngo, H. V., Martinetz, T., Born, J., and Mölle, M.: Auditory Closed-Loop Stimulation of the Sleep Slow Oscillation Enhances Memory: Neuron, vol. 78, pp. 545-553, 2013
  
• Schütze, H., Barth, E., and Martinetz, T.: Learning Orthogonal Bases for k-Sparse Representations: Workshop New Challenges in Neural Computation 2013, vol. 02, Hammer, B., Martinetz, T., and Villmann, T. (Ed.), pp. 119-120, 2013, Short Paper
  
• State, A., Coleca, F., Barth, E., and Martinetz, T.: Hand Tracking with an Extended Self-Organizing Map: Advances in Self-Organizing Maps, vol. 198, Estévez, P. A., Príncipe, J. C., and Zegers, P. (Ed.), Springer Berlin Heidelberg, pp. 115-124, 2013
  
• Wellner, M., Käster, T., Martinetz, T., and Barth, E.: Optimizing depth-of-field based on a range map and a wavelet transform: Mobile Computational Photography, vol. 8667D, Proceedings of SPIE, 2013
  

2012

• Binder, T., Kriener, F., Wichner, C., Wille, M., Wellner, M., Käster, T., Martinetz, T., and Barth, E.: How to make a small phone camera shoot like a big DSLR: creating and fusing multi-modal exposure series: Human Vision and Electronic Imaging XVII, vol. 8291, 2012
  
• Danielmeyer, H. G. and Martinetz, T.: The physics of business cycles and inflation: arXiv:1212.1282v1 [q-fin.GN], 2012
  
• Danielmeyer, H. G. and Martinetz, T.: Predicting economic growth with classical physics and human biology: arXiv:1212.1286 [q-fin.GN], 2012
  
• Danielmeyer, H. G. and Martinetz, T.: A physical theory of economic groth: arXiv:1206.2494v1 [q-fin.GN], 2012
  
• Hocke, J., Barth, E., and Martinetz, T.: Application of non-linear transform coding to image processing: Human Vision and Electronic Imaging XVII, vol. 8291, Rogowitz, B. E., Pappas, T. N., and Ridder, H. d. (Ed.), Proceedings of SPIE, 2012
  
• Hocke, J., Labusch, K., Barth, E., and Martinetz, T.: Sparse Coding and Selected Applications: KI - Künstliche Intelligenz, vol. 26, no. 4, Springer Berlin / Heidelberg, pp. 349-355, 2012
  
• Hocke, J. and Martinetz, T.: Experience in Training (Deep) Multi-Layer Perceptrons to Classify Digits: Workshop New Challenges in Neural Computation 2012, vol. 03, Hammer, B. and Villmann, T. (Ed.), pp. 113-115, 2012, Short Paper
  
• Schütze, H., Martinetz, T., Anders, S., and Mamlouk, A. M.: A Multivariate Approach to Estimate Complexity of FMRI Time Series: Artificial Neural Networks and Machine Learning - ICANN 2012, 22nd International Conference, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part II, vol. 7553, Villa, A. E., Duch, W., `Erdi, P., Masulli, F., and Palm, G. (Ed.), Springer, pp. 540-547, 2012
  
• Vig, E., Dorr, M., Martinetz, T., and Barth, E.: Intrinsic Dimensionality Predicts the Saliency of Natural Dynamic Scenes: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 6, IEEE, pp. 1080-1091, 2012
  
• Voigt, J., Krause, C., Rohwäder, E., Saschenbrecker, S., Hahn, M., Danckwardt, M., Feirer, C., Ens, K., Fechner, K., Barth, E., Martinetz, T., and Stöcker, W.: Automated Indirect Immunofluorescence Evaluation of Antinuclear Autoantibodies on HEp-2 Cells: Clinical and Developmental Immunology, vol. 2012, 2012, 7 pages
  
• Weigenand, A., Martinetz, T., and Claussen, J. C.: The phase response of the cortical slow oscillation: Cognitive Neurodynamics, vol. 6, no. 4, pp. 367-375, 2012
  

2011

• Hocke, J., Martinetz, T., and Barth, E.: Image Deconvolution with Sparse Priors: Workshop New Challenges in Neural Computation 2011, vol. 05, Hammer, B. and Villmann, T. (Ed.), , 2011, Abstract
  
• Kandaswamy, K. K., Chou, K., Martinetz, T., Möller, S., Suganthan, P. N., Sridharan, S., and Pugalenthi, G.: AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties: Journal of Theoretical Biology, vol. 270, no. 1, pp. 56-62, 2011
  
• Klement, S. and Martinetz, T.: On the Problem of Finding the Least Number of Features by L1-norm Minimisation: ICANN 2011, Part I, al., T. H. e. (Ed.), Springer, Heidelberg, pp. 315-322, 2011
  
• Kandaswamy, K. K., Pugalenthi, G., Hazrati, M. K., Kalies, K., and Martinetz, T.: BLProt: Prediction of bioluminescent proteins based on Support Vector Machine and Relief feature selection: BMC Bioinformatics, 2011
  
• Labusch, K., Barth, E., and Martinetz, T.: Robust and Fast Learning of Sparse Codes With Stochastic Gradient Descent: IEEE Transactions on Selected Topics in Signal Processing, vol. 5, no. 5, pp. 1048-1060, 2011
  
• Vig, E., Dorr, M., Martinetz, T., and Barth, E.: Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes: Cognitive Computation, vol. 3, no. 1, Springer New York, pp. 79-88, 2011
  
• Zhang, J., Mamlouk, A. M., Martinetz, T., Chang, S., Wang, J., and Hilgenfeld, R.: PhyloMap: an algorithm for visualizing relationships of large sequence data sets and its application to the influenza A virus genome: BMC Bioinformatics, vol. 12, , 2011
  
• Labusch, K., Barth, E., and Martinetz, T.: Soft-competitive Learning of Sparse Codes and its Application to Image Reconstruction: Neurocomputing, vol. 74, no. 9, pp. 1418-1428, 04 2011
  

2010

• Binder, T. and Martinetz, T.: On the boundedness of an iteration involving points on the hypersphere: arXiv:1001.1624v2 [cs.CG], 2010
  
• Böhme, M., Haker, M., Martinetz, T., and Barth, E.: Shading constraint improves accuracy of time-of-flight measurements: Computer Vision and Image Understanding, vol. 114, pp. 1329-1335, 2010
  
• Braenne, I., Labusch, K., Martinetz, T., and Mamlouk, A. M.: Interpretive Risk Assessment on GWA Data with Sparse Linear Regression: Machine Learning Reports, pp. 61-68, 2010
  
• Danielmeyer, H. G. and Martinetz, T.: The Biologic Stability of the Industrial Evolution: European Review (Academia Europea), vol. 18, no. 2, pp. 263-268, 2010
  
• Dorr, M., Martinetz, T., Gegenfurtner, K., and Barth, E.: Variability of eye movements when viewing dynamic natural scenes: Journal of Vision, vol. 10, no. 10, pp. 1-17, 2010
  
• Kandaswamy, K. K., Pugalenthi, G., Hartmann, E., Kalies, K., Möller, S., Suganthan, P., and Martinetz, T.: SPRED: A machine learning approach for the identification of classical and non-classical secretory proteins in mammalian genomes: Biochemical and biophysical research communications, vol. 391, no. 3, pp. 1306-1311, 2010
  
• Kandaswamy, K. K., Pugalenthi, G., Möller, S., Hartmann, E., Kalies, K., .N.Suganthan, ., and Martinetz, T.: Prediction of apoptosis protein locations with Genetic Algorithms and Support Vector Machines through a new mode of pseudo amino acid composition: Protein Peptide Letters, vol. 17, pp. 1473-1479, 2010
  
• Klement, S. and Martinetz, T.: The Support Feature Machine for Classifying with the Least Number of Features: Artificial Neural Networks - ICANN 2010, 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part II, vol. 6353, Diamantaras, K. I., Duch, W., and Iliadis, L. S. (Ed.), Springer, pp. 88-93, 2010
  
• Klement, S. and Martinetz, T.: A new approach to classification with the least number of features: Proceedings of the 9th International Conference on Machine Learning and Applications - ICMLA 2010, Washington, D.C, USA, 12-14 December, 2010, IEEE Computer Society, pp. 141-146, 2010
  
• Labusch, K., Barth, E., and Martinetz, T.: Bag of Pursuits and Neural Gas for Improved Sparse Coding: Proceedings of the 19th International Conference on Computational Statistics, Saporta, G. (Ed.), Springer, pp. 327-336, 2010
  
• Labusch, K. and Martinetz, T.: Learning Sparse Codes for Image Reconstruction: Proceedings of the 18th European Symposium on Artificial Neural Networks, Verleysen, M. (Ed.), D-Side Publishers, pp. 241-246, 2010
  
• Pugalenthi, G., Kandaswamy, K. K., Suganthan, P. N., .Sowdhamini, ., Martinetz, T., and Kolatkar, P.: SMpred: A Support Vector Machine Approach to Identify Structural Motifs in Protein Structure Without Using Evolutionary Information: Journal of Biomolecular Structure and Dynamics, vol. 28, no. 3, 2010
  
• Timm, F. and Martinetz, T.: Statistical Fourier Descriptors for Defect Image Classification: Proceedings of the 20th Int. Conference on Pattern Recognition (ICPR), IEEE Computer Society Press, Istanbul, Turkey, pp. 4190-4193, 2010
  
• Timm, F., Martinetz, T., and Barth, E.: Optical Inspection of Welding Seams: Computer Vision, Imaging and Computer Graphics: Theory and Applications, Revised Selected Papers, vol. 68, Springer Series Communications in Computer Science and Information Science, pp. 269-282, 2010
  
• Vig, E., Dorr, M., Martinetz, T., and Barth, E.: A Learned Saliency Predictor for Dynamic Natural Scenes: ICANN 2010, Part III, vol. 6354, Diamantaras, K., Duch, W., and Iliadis, L. S. (Ed.), Springer, Thessaloniki, Greece, pp. 52-61, 2010
  
• Weigenand, A., Ngo, H. V., Higgins, D., Martinetz, T., and Claussen, J. C.: Switching between Up and Down States in a conductance-based cortex model: Proceedings of the International Biosignal Processing Conference, Berlin, Germany, pp. 105:1-3, 2010
  

2009

• Böhme, M., Haker, M., Martinetz, T., and Barth, E.: Head Tracking With Combined Face And Nose Detection: Proceedings of the IEEE International Symposium on Signals, Circuits & Systems (ISSCS), Iasi, Romania, pp. 1-4, 2009
  
• Böhme, M., Haker, M., Riemer, K., Martinetz, T., and Barth, E.: Face Detection Using a Time-of-Flight Camera: Dynamic 3D Imaging - Workshop in Conjunction with DAGM, vol. 5742, pp. 167-176, 2009, http://www.springerlink.com/content/023881623j67r336/
  
• Danielmeyer, H. G. and Martinetz, T.: An exact theory of the industrial evolution and national recovery: , no. SIIM-TR-A-09-05, version 11-09, 2009
  
• Haker, M., Böhme, M., Martinetz, T., and Barth, E.: Deictic Gestures With A Time-of-Flight Camera: Gesture in Embodied Communication and Human-Computer Interaction - International Gesture Workshop GW 2009, vol. 5934, Kopp, S. and Wachsmuth, I. (Ed.), Springer, pp. 110-121, 2009, http://www.springerlink.com/content/uw12051512tk5261/
  
• Haker, M., Böhme, M., Martinetz, T., and Barth, E.: Self-Organizing Maps for Pose Estimation with a Time-of-Flight Camera: Dynamic 3D Imaging - Workshop in Conjunction with DAGM, vol. 5742, pp. 142-153, 2009, http://www.springerlink.com/content/006305183070t383/
  
• Haker, M., Martinetz, T., and Barth, E.: Multimodal Sparse Features for Object Detection: Artificial Neural Networks - ICANN 2009, 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, vol. 5769, Springer, pp. 923-932, 2009, http://www.springerlink.com/content/574230078m0228wh/
  
• Labusch, K., Barth, E., and Martinetz, T.: Sparse Coding Neural Gas: Learning of Overcomplete Data Representations: Neurocomputing, vol. 72, no. 7-9, pp. 1547-1555, 2009
  
• Labusch, K., Barth, E., and Martinetz, T.: Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas: Advances in Self-Organizing Maps - WSOM 2009, 7th International Workshop, St. Augustine, Fl, USA, June 2009, vol. 5629, Principe, J. and Miikkulainen, R. (Ed.), Springer, series Lecture Notes in Computer Science, pp. 145-153, 2009
  
• Labusch, K., Barth, E., and Martinetz, T.: Demixing Jazz-Music: Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources: Neural Network World, vol. 19, no. 5, Institute of Information and Computer Technology ASCR; Faculty of Transport, Czech Polytechnic University, Prague, pp. 561-579, 2009
  
• Martinetz, T., Labusch, K., and Schneegass, D.: SoftDoubleMaxMinOver: Perceptron-like Training of Support Vector Machines: IEEE Transactions on Neural Networks, vol. 20, no. 7, pp. 1061-1072, 2009
  
• Repsilber, D., Martinetz, T., and Björklund, M.: Adaptive Dynamics of Regulatory Networks: Size Matters: EURASIP Journal on Bioinformatics and Systems Biology, vol. 2009, 2009, Article ID 618502, 10 pages, doi:10.1155/2009/618502
  
• Timm, F., Klement, S., Martinetz, T., and Barth, E.: Welding Inspection Using Novel Specularity Features and A One-Class SVM: Proceedings of the Int. Conference on Computer Theory and Applications (VISAPP), vol. 1, INSTICC, Lisboa, Portugal, pp. 146-153, 2009
  

2008

• Böhme, M., Dorr, M., Graw, M., Martinetz, T., and Barth, E.: A Software Framework for Simulating Eye Trackers: Proceedings of Eye Tracking Research & Applications (ETRA), pp. 251-258, 2008,, copyright ACM, 2008. This is the author`s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Eye Tracking Research & Applications 2008, Savannah, Georgia,
  
• Böhme, M., Dorr, M., Martinetz, T., and Barth, E.: A Temporal Multiresolution Pyramid for Gaze-Contingent Manipulation of Natural Video: Passive Eye Monitoring, Hammoud, R. I. (Ed.), Springer, pp. 225-243, 2008
  
• Böhme, M., Haker, M., Martinetz, T., and Barth, E.: Shading Constraint Improves Accuracy of Time-of-Flight Measurements: CVPR 2008 Workshop on Time-of-Flight-based Computer Vision (TOF-CV), 2008
  
• Böhme, M., Haker, M., Martinetz, T., and Barth, E.: A facial feature tracker for human-computer interaction based on 3D Time-of-Flight cameras: Int. J. on Intell. Systems Techn. and App., Issue on Dynamic 3D Imaging, vol. 5, no. 3, pp. 264-273, 2008
  
• Dorr, M., Vig, E., Gegenfurtner, K. R., Martinetz, T., and Barth, E.: Eye movement modelling and gaze guidance: Fourth International Workshop on Human-Computer Conversation, 2008
  
• Haker, M., Böhme, M., Martinetz, T., and Barth, E.: Scale-Invariant Range Features for Time-of-Flight Camera Applications: CVPR 2008 Workshop on Time-of-Flight-based Computer Vision (TOF-CV), pp. 1-6, 2008
  
• Klement, S., Madany Mamlouk, A., and Martinetz, T.: Reliability of Cross-Validation for SVMs in High-Dimensional, Low Sample Size Scenarios: Artificial Neural Networks - ICANN 2008, 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II, vol. 5163, Kurková, V., Neruda, R., and Koutn`ik, J. (Ed.), Springer, pp. 41-50, 2008, http://www.springerlink.com/content/a852001534335865/
  
• Labusch, K., Barth, E., and Martinetz, T.: Learning data representations with Sparse Coding Neural Gas: Proceedings of the 16th European Symposium on Artificial Neural Networks, Verleysen, M. (Ed.), D-Side Publishers, pp. 233-238, 2008
  
• Labusch, K., Barth, E., and Martinetz, T.: Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources: Artificial Neural Networks - ICANN 2008, 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II, vol. 5163, Kurková, V., Neruda, R., and Koutn`ik, J. (Ed.), Springer, pp. 788-797, 2008
  
• Labusch, K., Barth, E., and Martinetz, T.: Simple Method for High-Performance Digit Recognition Based on Sparse Coding: IEEE Transactions on Neural Networks, vol. 19, no. 11, pp. 1985-1989, 2008
  
• Labusch, K., Timm, F., and Martinetz, T.: Simple Incremental One-Class Support Vector Classification: Pattern Recognition - Proceedings of the DAGM, Rigoll, G. (Ed.), pp. 21-30, 2008
  
• Schneegass, D., Udluft, S., and Martinetz, T.: Uncertainty Propagation for Quality Assurance in Reinforcement Learning: Proc. of the International Joint Conference on Neural Networks, pp. 2588-2595, 2008
  
• Timm, F., Klement, S., and Martinetz, T.: Fast Model Selection for MaxMinOver-based Training of Support Vector Machines: Proceedings of the 19th Int. Conference on Pattern Recognition (ICPR), IEEE Computer Society Press, Tampa, Florida, USA, 2008
  

2007

• Dorr, M., Böhme, M., Martinetz, T., and Barth, E.: Gaze beats mouse: a case study: The 3rd Conference on Communication by Gaze Interaction - COGAIN 2007, Leicester, UK, pp. 16-19, 2007
  
• Haker, M., Böhme, M., Martinetz, T., and Barth, E.: Geometric Invariants for Facial Feature Tracking with 3D TOF Cameras: Proceedings of the IEEE International Symposium on Signals, Circuits & Systems (ISSCS), vol. 1, Iasi, Romania, pp. 109-112, 2007
  
• Labusch, K., Siewert, U., Martinetz, T., and Barth, E.: Learning optimal features for visual pattern recognition: Human Vision and Electronic Imaging XII, vol. 6492, Rogowitz, B. E., Pappas, T. N., and Daly, S. J. (Ed.), Proceedings of SPIE, 2007
  
• Schneegass, D., Schaefer, A. M., and Martinetz, T.: The Intrinsic Recurrent Support Vector Machine: Proc. of the European Symposium on Artificial Neural Networks, Verleysen, M. (Ed.), pp. 325-330, 2007
  
• Schneegass, D., Udluft, S., and Martinetz, T.: Explicit Kernel Rewards Regression for Data-Efficient Near-optimal Policy Identification: Proc. of the European Symposium on Artificial Neural Networks, Verleysen, M. (Ed.), pp. 337-342, 2007
  
• Schneegass, D., Udluft, S., and Martinetz, T.: Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification: Proc. of the International Conference on Artificial Neural Networks, pp. 109-118, 2007
  
• Schneegass, D., Udluft, S., and Martinetz, T.: Neural Rewards Regression for Near-Optimal Policy Identification in Markovian and Partial Environments: Proc. of the European Symposium on Artificial Neural Networks, Verleysen, M. (Ed.), pp. 301-306, 2007
  

2006

• Barth, E., Dorr, M., Böhme, M., Gegenfurtner, K. R., and Martinetz, T.: Guiding the mind`s eye: improving communication and vision by external control of the scanpath: Human Vision and Electronic Imaging, vol. 6057, Rogowitz, B. E., Pappas, T. N., and Daly, S. J. (Ed.), 2006, Invited contribution for a special session on Eye Movements, Visual Search, and Attention: a Tribute to Larry Stark.
  
• Barth, E., Dorr, M., Böhme, M., Gegenfurtner, K. R., and Martinetz, T.: Guiding Eye Movements for Better Communication and Augmented Vision: Perception and Interactive Technologies, vol. 4021, Springer, pp. 1-8, 2006
  
• Böhme, M., Dorr, M., Krause, C., Martinetz, T., and Barth, E.: Eye Movement Predictions on Natural Videos: Neurocomputing, vol. 69, no. 16-18, pp. 1996-2004, 2006,
  
• Böhme, M., Dorr, M., Martinetz, T., and Barth, E.: Gaze-Contingent Temporal Filtering of Video: Proceedings of Eye Tracking Research & Applications (ETRA), pp. 109-115, 2006,, copyright ACM, 2006. This is the author`s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Eye Tracking Research & Applications 2006, San Diego, Californ
  
• Böhme, M., Meyer, A., Martinetz, T., and Barth, E.: Remote Eye Tracking: State of the Art and Directions for Future Development: The 2nd Conference on Communication by Gaze Interaction - COGAIN 2006, Turin, Italy, pp. 10-15, 2006
  
• Dorr, M., Böhme, M., Martinetz, T., and Barth, E.: Gaze-Contingent Spatio-Temporal Filtering in a Head-Mounted Display: Perception and Interactive Technologies, vol. 4021, Springer, pp. 205-207, 2006
  
• Martinetz, T., Madany Mamlouk, A., and Mota, C.: Fast and Easy Computation of Approximate Smallest Enclosing Balls: Proc. SIBGRAPI, pp. 163-170, 2006
  
• Meyer, A., Böhme, M., Martinetz, T., and Barth, E.: A Single-Camera Remote Eye Tracker: Perception and Interactive Technologies, vol. 4021, Springer, pp. 208-211, 2006
  
• Polani, D., Nehaniv, C., Martinetz, T., and Kim, J. T.: Relevant Information in Optimized Persistence vs. Progeny Strategies: Proc. Artificial Life X, M.Rocha, L., Bedau, M., Floreano, D., Goldstone, R., Vespignani, A., and Yaeger, L. (Ed.), 2006
  
• Schneegass, D., Labusch, K., and Martinetz, T.: MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation: Artificial Neural Networks - ICANN 2006, Springer, Berlin, Heidelberg, pp. 150-158, 2006
  
• Schneegass, D., Martinetz, T., and Clausohm, M.: OnlineDoubleMaxMinOver: a simple approximate time and information efficient online Support Vector Classification method.: Proceedings of the 14th European Symposium on Artificial Neural Networks, Verleysen, M. (Ed.), pp. 575-580, 2006
  
• Schneegass, D., Udluft, S., and Martinetz, T.: Kernel Rewards Regression: An Information Efficient Batch Policy Iteration Approach.: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, Devedzic, V. (Ed.), pp. 428-433, 2006
  

2005

• Brinkschulte, U., Becker, J., Fey, D., Hochberger, C., Martinetz, T., Müller-Schloer, C., Schmeck, H., Ungerer, T., and Würtz, R.: ARCS 2005 - System Aspects in Organic and Pervasive Computing - Workshops Procedeedings, Innsbruck Austria, March 14-17: , Berlin, Offenbach, 2005
  
• Dorr, M., Böhme, M., Martinetz, T., and Barth, E.: Visibility of temporal blur on a gaze-contingent display: APGV 2005 ACM SIGGRAPH Symposium on Applied Perception in Graphics and Visualization, pp. 33-36, 2005
  
• Dorr, M., Böhme, M., Martinetz, T., and Barth, E.: Predicting, analysing, and guiding eye movements: Neural Information Processing Systems Conference (NIPS 2005), Workshop on Machine Learning for Implicit Feedback and User Modeling, 2005
  
• Martinetz, T., Labusch, K., and Schneegass, D.: SoftDoubleMinOver: A Simple Procedure for Maximum Margin Classification.: Artificial Neural Networks: Biological Inspirations. ICANN 2005: 15th International Conference. Proceedings, Part II, Duch, W., Kacprzyk, J., Oja, E., and Zadrozny, S. (Ed.), pp. 301-306, 2005
  
• Madany Mamlouk, A., Sharp, H., Menne, K. M. L., Hofmann, U. G., and Martinetz, T.: Unsupervised Spike Sorting with ICA and its Evaluation using GENESIS Simulations: Neurocomputing, vol. 65-66, pp. 275-282, 2005
  
• Meyer-Baese, A., Jancke, K., Wismueller, A., Foo, S., and Martinetz, T.: Medical image compression using topology-preserving neural networks: Engineering Applications of Artificial Intelligence, vol. 18, no. 4, pp. 383-392, 2005
  

2004

• Böhme, M., Krause, C., Barth, E., and Martinetz, T.: Eye Movement Predictions Enhanced by Saccade Detection: Brain Inspired Cognitive Systems, , 2004
  
• Böhme, M., Krause, C., Martinetz, T., and Barth, E.: Saliency Extraction for Gaze-Contingent Displays: Proceedings of the 34th GI-Jahrestagung, vol. 2, pp. 646-650, 2004, Workshop on Organic Computing
  
• Dorr, M., Martinetz, T., Gegenfurtner, K., and Barth, E.: Guidance of Eye Movements on a Gaze-Contingent Display: Dynamic Perception Workshop of the GI Section ``Computer Vision``, Ilg, U. J., Bülthoff, H. H., and Mallot, H. A. (Ed.), pp. 89-94, 2004
  
• Kim, J. T., Gewehr, J., and Martinetz, T.: Binding Matrix: A Novel Approach for Binding Site Recognition: Journal of Bioinformatics and Computational Biology, vol. 2, pp. 289-307, 2004
  
• Kim, J. T., Martinetz, T., and Polani, D.: On the Evolution of Information in the Constituents of Regulatory Gene: Function and Regulation of Cellular Systems. Experiments and Models, Deutsch, A., Howard, J., Falcke, M., and Zimmermann, W. (Ed.), Basel, pp. 259-264, 2004
  
• Madany Mamlouk, A. and Martinetz, T.: On the Dimensions of the Olfactory Perception Space: Neurocomputing, vol. 58-60, pp. 1019-1025, 2004
  
• Martinetz, T. and Madany Mamlouk, A.: Easy and Fast Computation of Approximate Smallest Enclosing Balls: , no. SIIM-TR-A-04-13, 2004
  
• Martinetz, T.: MinOver Revisited for Incremental Support-Vector-Classification: DAGM 2004, vol. 3175, Rasmussen, C., Buelthoff, H., Giese, M., and Schoelkopf, B. (Ed.), Springer-Verlag Berlin Heidelberg, pp. 187-194, 2004
  
• Martinetz, T.: MaxMinOver: A Simple Incremental Learning Procedure for Support Vector Classification: IEEE Proceedings of the International Joint Conference on Neural Networks (IJCNN 2004), Budapest, Hungary, pp. 2065-2070, 2004
  

2003

• Barth, E., Drewes, J., and Martinetz, T.: Individual predictions of eye-movements with dynamic scenes: Electronic Imaging 2003, vol. 5007, Rogowitz, B. E. and Pappas, T. N. (Ed.), pp. 252-259, 2003
  
• Barth, E., Drewes, J., and Martinetz, T.: Dynamic predictions of tracked gaze: Seventh International Symposium on Signal Processing and its Applications, Paris, 2003, Special Session on Foveated Vision in Image and Video Processing
  
• Kim, J. T., Martinetz, T., and Polani, D.: Bioinformatic Principles Underlying the Information Content of Transcription Factor Binding Sites: Journal of Theoretical Biology, vol. 220, pp. 529-544, 2003
  
• Martinetz, T., Gewehr, J., and Kim, J. T.: Statistical Learning for Detecting Protein-DNA-Binding Sites: Proceedings of the International Joint Conference on Neural Networks 2003, II, D. C. W., Hasselmo, M., and Venayagamoorthy, K. (Ed.), IEEE Press, pp. 2940-2945, 2003
  
• Madany Mamlouk, A., Kim, J. T., Barth, E., Brauckmann, M., and Martinetz, T.: One-Class Classification with Subgaussians: Pattern Recognition (DAGM 2003), vol. 2781, Michaelis, B. and Krell, G. (Ed.), Springer-Verlag Berlin Heidelberg, pp. 346-353, 2003
  
• Meyer-Bäse, A., Otto, T., Martinetz, T., Auer, D., and Wismüller, A.: Model-Free Functional MRI Analysis Using Topographic Independent Component Analysis: Proceedings of the European Symposium on Artificial Neural Networks (ESANN), vol. D-Side Publishers 4, pp. 509-514, 2003
  

2002

• Barth, E. and Martinetz, T.: Information technology for active perception: 8th Annual German-American Beckman Frontiers of Science Symposium, 2002, Poster in PDF
  
• Polani, D., Kim, J. T., and Martinetz, T.: Abstracting and Synthesizing the Principles of Living Systems. Proceedings of the Fifth German Workshop on Artificial Life: , infix / Akademische Verlagsgesellschaft Aka, Berlin, 2002
  
• Repsilber, D., Kim, J. T., Liljenström, H., and Martinetz, T.: Using Coarse-Grained, Discrete Systems for Data-Driven Inference of Regulatory Gene Networks: Perspectives and Limitations for Reverse Engineering: Proceedings of the Fifth German Workshop on Artificial Life, Polani, D., Kim, J. T., and Martinetz, T. (Ed.), infix / Akademische Verlagsgesellschaft Aka, Berlin, pp. 67-76, 2002
  

2001

• Haker, M., Meyer, A., Polani, D., and Martinetz, T.: A Method for Incorporation of New Evidence to Improve World State Estimation: Proceedings of the RoboCup 2001 Symposium, Seattle, pp. 356-361, 2001
  
• Kim, J. T., Martinetz, T., and Polani, D.: On the Effects of Transcription Factor Properties on the Information Content of Binding Sites: Proceedings of the German Conference on Bioinformatics 2001. German Research Center for Biotechnology (GBF), Maschroder Weg 1, 38124 Braunschweig, Germany, Wingender, E., Hofestädt, R., and Liebich, I. (Ed.), pp. 192-194, 2001
  
• Martinetz, T.: Bioinformatik - Informationsverarbeitung in der Biologie: Focus MUL, vol. 18, no. 2, pp. 74-81, 2001
  
• Polani, D. and Martinetz, T.: Team Description for Lucky Lübeck - Evidence Based Position Estimation: RoboCup-2000. Robot Soccer World Cup IV, LNCS, Stone, P., Balch, T., and Kraetzschmar, G. (Ed.), Springer, pp. 481-484, 2001
  
• Polani, D., Martinetz, T., and Kim, J. T.: An Information-Theoretic Approach for the Quantification of Relevance: Advances in Artificial Life (Proc. 6th European Conference on Artificial Life), Kelemen, J. and Sosik, P. (Ed.), Springer-Verlag, 2001
  
• Polani, D., Martinetz, T., and Kim, J. T.: On the Quantification of Relevant Information: , 2001, Presented at SCAI`01 (Scandinavian Conference on Artificial Intelligence), Feb. 19-21, 2001
  
• Ronnewinkel, C. and Martinetz, T.: Explicit Speciation with few a priori Parameters for Dynamic Optimization Problems: GECCO 2001 - Workshop Proceedings, Morgan Kaufmann, San Francisco, 2001
  
• Ronnewinkel, C., Wilke, C., and Martinetz, T.: Genetic algorithms in time-dependent environments: L. Kallel, B. Naudts, and A. Rogers, editors, Theoretical Aspects of Evolutionary Computing, Natural Computing, Springer, pp. 261-285, 2001
  
• Wilke, C. O., Ronnewinkel, C., and Martinetz, T.: Dynamic Fitness Landscapes in Molecular Evolution: Physics Reports, vol. 349, pp. 395-446, 2001
  

1999

• Wilke, C. and Martinetz, T.: Lifetimes of agents under external stress: Phys. Rev. E, Rapid Communication, vol. 59(3), pp. R2512-R2515, 1999
  
• Wilke, C. O. and Martinetz, T.: Adaptive walks on time-dependent fitness landscapes: Physical Review E, vol. 60, no. 2, pp. 2154-2159, 1999
  
• Wilke, C. O., Ronnewinkel, C., and Martinetz, T.: Molecular Evolution in Time Dependent Environments: Advances in Artificial Life, ECAL 1999, Floreano, D., Nicoud, J., and Mondada, F. (Ed.), Springer, 1999
  

1998

• Altmeyer, S., Wilke, C. O., and Martinetz, T.: How fast do structures emerge in hypercycle systems?: Third German Workshop on Artificial Life, Wilke, C. O., Altmeyer, S., and Martinetz, T. (Ed.), Verlag Harri Deutsch, 1998
  
• Wilke, C. O., Altmeyer, S., and Martinetz, T.: Third German Workshop on Artificial Life: , Verlag Harri Deutsch, 1998
  
• Wilke, C. O., Altmeyer, S., and Martinetz, T.: Large-scale evolution and extinction in a hierarchically structured environment: Proceedings of Artificial Life VI, Los Angeles, June 26-29, 1998, Adami, C., Belew, R., Kitano, H., and Taylor, C. (Ed.), MIT Press, 1998
  
• Wilke, C. O., Altmeyer, S., and Martinetz, T.: Aftershocks in Coherent-Noise Models: Physica D, vol. 120, pp. 401-417, 1998
  
• Wilke, C. O. and Martinetz, T.: A coarse-grained model of evolution with variable system size: 2nd German Workshop on Artificial Life, Dortmund 1998 SYS Report, Internal Report of Systems Analysis Research Group, University of Dortmund, Department of Computer Science, Dortmund, Germany, Dittrich, P., Rauhe, H., and Banzhaf, W. (Ed.), 1998
  
• Wilke, C. and Martinetz, T.: Hierarchical noise in large systems of independent agents: Phys. Rev. E, vol. 58(6), pp. 7101-7108, 1998
  

1997

• Villmann, T., Der, R., Herrmann, M., and Martinetz, T.: Topology Preservation in Self-Organizing Feature Maps: Exact Definition and Measurement: IEEE-Transactions on Neural Networks, vol. 8, no. 2, pp. 256-266, 1997
  
• Wilke, C. O. and Martinetz, T.: A Simple Model of Evolution with Variable System Size: Phys. Rev. E, vol. 56, pp. 7128-7131, 1997
  

1996

• Danielmeyer, H. G. and Martinetz, T.: Best practice code of the industrial society - the Forum Engelberg model: European Review (Academia Europea), 1996
  
• Danielmeyer, H. G. and Martinetz, T.: Innovation, investment, and sustainable growth: Acta 7. Forum Engelberg, 1996
  
• Martinetz, T., Gramckow, O., Protzel, P., and Sörgel, G.: Neuronale Netze zur Steuerung von Walzstrassen: atp - Automatisierungstechnische Praxis, vol. 10, 1996
  

1995

• Martinetz, T. and Hollatz, J.: Neuro-Fuzzy in der Prozessautomatisierung: Neuro-Fuzzy in der industriellen Automatisierung, Bonfig, K. W. (Ed.), expert-Verlag, Renningen, pp. 135-144, 1995
  
• Martinetz, T., Protzel, P., and Gramckow, O.: Walzwerksteuerung mit neuronalen Netzen: Neuronale Netze: Anwendungen in der Automatisierungstechnik, Bericht 1184, VDI-Verlag, Düsseldorf, pp. 35-42, 1995, Auch im Jahrbuch 1997 der VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik, pages 333-340, Düsseldorf 1995. VDI-Verlag
  
• Martinetz, T., Protzel, P., Gramckow, O., and Sörgel, G.: Neural Network Control for Steel Rolling Mills: Neural Networks: Artificial Intelligence and Industrial Applications, Kappen, B. and Gielen, S. (Ed.), Springer, Heidelberg, pp. 280-286, 1995
  
• Martinetz, T.: Lernverfahren für komplexe Aktuatorik/Robotik: Neuro-Fuzzy Technologien - Anwendungen, Zimmermann, H. J. (Ed.), VDI-Verlag, Düsseldorf, pp. 89-101, 1995
  

1994

• Deco, G. and Martinetz, T.: Regularizing stochastic Pott neural networks by penalizing mutual information: Proceedings of the International Conference on Artificial Neural Networks (ICANN-94), Sorrent, Marinaro, M. and Morasso, P. (Ed.), Springer, Heidelberg, pp. 693-696, 1994
  
• Martinetz, T. and Poppe, T.: A neural network approach to estimating material properties: Proceedings of the World Congress on Neural Networks (WCNN-94), San Diego, pp. 541-544, 1994
  
• Martinetz, T., Protzel, P., Gramckow, O., and Sörgel, G.: Neural network control for rolling mills: Proceedings of the Second European Congress on Intelligent Techniques and Soft Computing (EUFIT-94), Aachen, vol. I, pp. 147-152, 1994
  
• Martinetz, T. and Schulten, K.: Topology Representing Networks: Neural Networks, vol. 7(3), pp. 507-522, 1994
  
• Martinetz, T.: Neural learning can form structures from computational geometry: Tagungsband des 39. internationalen wissenschaftlichen Kolloquiums, Ilmenau, vol. II, pp. 206-211, 1994
  
• Poppe, T., Martinetz, T., and Hohendahl, K.: Optimierte Steuerung eines Elektrolichtbogenofens: Neue Techniken der Informationsverarbeitung, Schlang, M., Schürmann, B., and Linzenkirchner, E. (Ed.), Winkler-Verlag, München, pp. 19-29, 1994
  
• Villmann, T., Der, R., Herrmann, M., and Martinetz, T.: Topology preservation in self-organizing feature maps: general definition and efficient measurement: Lecture Notes in Computer Science, Springer, Heidelberg, pp. 159-166, 1994
  
• Villmann, T., Der, R., and Martinetz, T.: A new quantitative measure of topology preservation in Kohonen`s feature maps: Proceedings of the IEEE International Conference on Neural Networks (ICNN-94), Orlando, vol. II, pp. 645-648, 1994
  
• Villmann, T., Der, R., and T. Martinetz, t. . A. n. a. t. m. t. t. p. o. f. m.: : Proceedings of the International Conference on Artificial Neural Networks (ICANN-94), Sorrent, Marinaro, M. and Morasso, P. (Ed.), Springer, Heidelberg, pp. 298-301, 1994
  

1993

• Martinetz, T., Berkovich, S., and Schulten, K.: Neural-gas Network for Vector Quantization and its Application to Time-Series Prediction: IEEE-Transactions on Neural Networks, vol. 4, no. 4, pp. 558-569, 1993
  
• Martinetz, T. and Schulten, K.: A neural network for robot control: cooperation between neural units as a requirement for learning: Computers & Electrical Engineering, vol. 19, no. 4, pp. 315-332, 1993
  
• Martinetz, T. and Schulten, K.: A Neural Network with Hebbian-like Adaption Rules Learning Visuomotor Coordination of a PUMA Robot: Proceedings of the IEEE International Conference on Neural Networks (ICNN-93), San Francisco, vol. II, pp. 820-825, 1993
  
• Martinetz, T.: Competitive hebbian learning rule forms perfectly topology preserving maps: Proceedings of the International Conference on Artificial Neural Networks (ICANN-93), Amsterdam, Gielen, S. and Kappen, B. (Ed.), Springer, Heidelberg, pp. 427-434, 1993
  
• Martinetz, T.: Neuronale Karten in der Robotik: Lernen visuell geführter Greifbewegungen: Konferenzband des 3. Anwendersymposiums zu Neuro-Fuzzy Technologien, Bochum, 1993
  
• Poppe, T. and Martinetz, T.: Estimating material properties for process optimization: Proceedings of the International Conference on Artificial Neural Networks (ICANN-93), Amsterdam, Gielen, S. and Kappen, B. (Ed.), Springer, Heidelberg, pp. 795-798, 1993
  

1992

• Martinetz, T.: Selbstorganisierende neuronal Netzwerkmodelle zur Bewegungssteuerung: , Sankt Augustin, 1992, Dissertationen zur künstlichen Intelligenz
  
• Ritter, H., Martinetz, T., and Schulten, K.: Neural Computation and Self-Organizing Maps: An Introduction: , Addison-Wesley, Massachusetts, 1992
  

1991

• Berkovich, S., Dalger, P., Hesselroth, T., Martinetz, T., Noël, B., Walter, J., and Schulten, K.: Vector quantization algorithm for time-series prediction and visuo-motor control of robots: Verteilte Künstliche Intelligenz und Kooperatives Arbeiten, Brauer, H. W. (Ed.), Heidelberg, pp. 443-447, 1991
  
• Martinetz, T. and Schulten, K.: A Neural-Gas Network Learns Topologies: Artificial Neural Networks, vol. I, pp. 397-402, 1991
  
• Ritter, H., Martinetz, T., and Schulten, K.: Neuronale Netze - eine Einführung in die Neuroinformatik selbstorganisierender Netzwerke: , Addison-Wesley, Bonn, 1991
  
• Walter, J., Martinetz, T., and Schulten, K.: Industrial Robot learns Visuo-motor Coordination by Means of Neural-Gas Network: Artificial Neural Networks, vol. I, al., T. K. e. (Ed.), Amsterdam, pp. 357-364, 1991
  

1990

• Martinetz, T., Ritter, H., and Schulten, K.: Learning of Visuomotor-Coordination of a Robot Arm with Redundant Degrees of Freedom: Proceedings of the International Conference on Parallel Processing in Neural Sytems and Computers (ICNC-90), Düsseldorf 1990, Eckmiller, R., Hartmann, G., and Hauske, G. (Ed.), Amsterdam, pp. 431-434, 1990, And in: Proceedings of the Third International Symposium on Robotics and Manufacturing, Vancouver 1990 (ISRAM-90), pages 521-526, 1990
  
• Martinetz, T., Ritter, H., and Schulten, K.: Three-Dimensional Neural Net for Learning Visuomotor Coordination of a Robot Arm: IEEE-Transactions on Neural Networks, vol. 1, no. 1, pp. 131-136, 1990
  
• Martinetz, T. and Schulten, K.: Hierarchical Neural Net for Learning Control of a Robot`s Arm and Gripper: Proceedings of the International Joint Conference on Neural Networks (IJCNN-90), San Diego 1990, vol. II, pp. 747-752, 1990
  
• Ritter, H., Martinetz, T., and Schulten, K.: Neuronale Netze - eine Einführung in die Neuroinformatik selbstorganisierender Netzwerke: , Addison-Wesley, Bonn, 1990
  

1989

• Martinetz, T., Ritter, H., and Schulten, K.: 3D-Neural-Net for Learning Visuomotor-Coordination of a Robot Arm: Proceedings of the International Joint Conference on Neural Networks (IJCNN-89), Washington 1989, vol. II, pp. 351-356, 1989
  
• Martinetz, T., Ritter, H., and Schulten, K.: Kohonen`s Self-organizing Map for Modeling the Formation of the Auditory Cortex of a Bat: Connectionism in Perspective, Pfeifer, R., Schreter, Z., Fogelman-Soulie, F., and Steels, L. (Ed.), Amsterdam, pp. 403-412, 1989
  
• Ritter, H., Martinetz, T., and Schulten, K.: Ein Gehirn für Roboter - Wie neuronale Netzwerke Roboter steuern können: MC-Computermagazin, vol. 2, pp. 48-61, 1989
  
• Ritter, H., Martinetz, T., and Schulten, K.: Topology-Conserving Maps for Learning Visuo-Motor-Coordination: Neural Networks, vol. 2, pp. 159-168, 1989
  
• Ritter, H., Martinetz, T., and Schulten, K.: Topology-Conserving Maps for Motor Control: Neural Networks, from Models to Applications, Personnaz, L. and Dreyfus, G. (Ed.), EZIDET, pp. 579-591, 1989
  

1988

• Martinetz, T.: Selbstorganisierte visuo-motorische Kopplung: , 1988, Diploma Thesis