Giese, M. A (2002). Learning recurrent neural models with minimal complexity from sparse neural data . NATO Workshop on Learning Theory and Practice, Leuven.
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Ilg, W. & Giese, M. A. (2002). Modeling of movement sequences based on hierarchical spatio-temporal correspondences of movement primitives. Bülthoff H H, Lee S W, Poggio T, Wallraven C (eds.): Biologically motivated Computer Vision, 528-537.
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Ilg, W. & Giese, M. A. (2002). Modeling of movement sequences based on hierarchical spatial-temporal correspondences of movement primitives. In Wuerz RP, Lappe M (eds): Dynamic Perception, 127-132.
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Giese, M. A., Jastorff, J. & Kourtzi, Z. (2002). Learning of the discrimination of artificial complex biological motion. Dynamic Perception; Infix, Berlin, 133-138..
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Xie, X. & Giese, M. A. (2002). Nonlinear dynamics of direction-selective recurrent neural media. Phys Rev E Stat Nonlin Soft Matter Phys. 65 (5 Pt 1):051904 (2002)., 65(5, Pt. 1).
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Yu, A. J., Poggio, T. A. & Giese, M. A. (2002). Biophysiologically plausible implementations of maximum operation. Neural Computation, 14(12), 2857-2881.
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Giese, M. A. & Lappe, M. (2002). Measurement of generalization fields for the recognition of biological motion. Vision Researech, 38, 1847-1858.
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Giese, M. A. & Xie, X. (2002). Exact solution of the nonlinear dynamics of recurrent neural mechanisms for direction selectivity. Neurocomputing,44-46(C): 417-422., 44-46(C), 417-422.
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Giese, M. A. (2002). Prototypes of biological movements in brains and machines. Biologically motivated Computer Vision, 157-170.
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Giese, M. A., Knappmeyer, B. & B\"ulthoff, H. H. (2002). Automatic synthesis of sequences of human movements by linear combination of learned example patterns. Bulthoff H H, Lee S W, Poggio T, Wallraven C (eds.): Biologically motivated Computer Vision, 538-547.
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Poggio, T. A. & Giese, M. A. (2002). Neural mechanisms for the recognition of biological movements and actions. AI Memo #AI Memo 2002-012/CBCL Paper #219. Massachusetts Institute of Technology, Cambridge, MA.
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Giese, M. A. & Xie, X. (2002). Exact solution of the nonlinear dynamics of recurrent neural mechanisms for direction selectivity. Phd Thesis.
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Jastorff, J., Kourtzi, Z. & Giese, M. A. (2002). Recognition of artificial complex biological motion generated by motion morphing. 5. Inderdisciplinary College 2002, Günne, Germany. POSTER AWARD.
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Giese, M. A. (2002). Hierarchical neural model for the recognition of biological motion. Journal of Vision, 1(3), 356a.
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Jastorff, J. & Giese, M. A. (2002). Neural model for the learning of biological motion. Perception, 31(119).
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Jastorff, J., Kourtzi, Z. & Giese, M. A. (2002). Role of learning in biological motion recognition. 2nd Workshop on Biologically Motivated Computer Vision, T\"ubingen, Germany..
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Ilg, W., Churan, J. & Giese, M. A. (2002). Biologische Bewegung definiert durch Bewegung 2. Ordnung. In: Bülthoff H H, Gegenfurtner K R, Mallot H P, Ulrich R: Beiträge zur 5. Tübinger Wahrnehmungskonferenz. 121.
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Ilg, W., Golla, H., Giese, M. A. & Thier, P. (2002). Quantitative Movement Analysis based on Hierarchical Spatial Temporal Correspondence of Movement Primitives. 11th Annual Meeting of the European Society for Movement Analysis in Adults and Children. Leuven,.
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Vaina, L. M. & Giese, M. A. (2002). Neuronal plausibles Modell f\"ur die Erkennung biologischer Bewegug. In: B\"ulthoff H. H., Gegenfurtner K R, Mallot H P, Ulrich R: Beiträge zur 5. T\"ubinger Wahrnehmungskonferenz. 110.
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Jastorff, J., Kourtzi, Z. & Giese, M. A. (2002). Learning of artificial biological motion patterns. 32nd Annual Meeting of the Society for Neuroscience, Orlando, United States..
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[Bibtex]
Jastorff, J., Kourtzi, Z. & Giese, M. A. (2002). Learning of the discrimination of artificial complex biological motion. In: Wuerz RP, Lappe M (eds): Dynamic Perception, 133-138.
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[Bibtex]
Giese, M. A. (2002). Biologische Bewegung definiert durch Bewegung 2. Ordnung. In: Baumann M, Keinath A, Krems J F (eds.): Abstracts zur 44. Tagung Experimentell Arbeitender Psychologen. TU Chenitz, March, 2002, 23.
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Casile, A. & Giese, M. A. (2002). Critical Features for biological Motion. 2nd Workshop on Biologically Motivated Computer Vision, T\"ubingen, Germany..
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Giese, M. A. & Poggio, T. A. (2002). Biologically Plausible Neural Model for the Recognition of Biological Motion and Actions. Phd Thesis.
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Moukovski, A., Gorinevski, D. M., Giese, M. A. & von Seelen, W. (2001). Formation of pinheels of preferred orientation by learning sparse neural representations of natural images. Neurocomputing 44-46(C):549-553. (reprinted in "Computational Neuroscience: Trends in Research" 2002, 1st ed., ISBN13:9780444511348, Elsevier Science {&} Technology.).
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Xie, X. & Giese, M. A. (2001). Generating velocity tuning by asymmetric recurrent connections. In: T. G. Dietterich and S. Becker and Z. Ghahramani (eds): Advances in Neural Information Processing Systems, 14.
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Yu, A. J., Poggio, T. A. & Giese, M. A. (2001). Biologically plausible neural circuits for realization of maximum operations. Phd Thesis.
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Vaina, L. M. & Giese, M. A. (2001). Pathways in the analysis of biological motion: computational model and fMRI results. Perception, 23(suppl.), 116.
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Giese, M. A. & Poggio, T. A. (2000). Quantification and Classification of Locomotion Patterns by Spatio-temporal Morphable Models. In: Third IEEE Workshop on Visual Surveillance, Dublin, Ireland, July 1, 2000.
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Giese, M. A. & Poggio, T. A. (2000). Morphable models for the analysis and synthesis of complex motion patterns. International Journal of Computer Vision, 38(1), 59-73.
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Giese, M. A. (2000). Neural field model for the recognition of biological motion. Second International ICSC Symposium on Neural Computation NC 2000, May 23-26, Berlin, Germany.
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Giese, M. A. (2000). Neural Model for the Recognition of Biological Motion. In: Dynamische Perzeption 2, G. Baratoff and H. Neumann (eds.), 105-110.
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Giese, M. A. (2000). Learning-based neural model for the recognition of biological motion. Conference of the Federation of European Neuroscience Research..
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Yu, A. J., Poggio, T. A. & Giese, M. A. (2000). Neural mechanisms for the realization of maximum operations. Conference of the Federation of European Neuroscience Research, Brighton, June, 2000..
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Yu, A. J., Poggio, T. A. & Giese, M. A. (2000). Neural circuits for the realization of maximum operation. Society of Neuroscience Abstracts, 26, 1202.
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Lappe, M. & Giese, M. A. (2000). Categorization of biological movement patterns. Investigative Ophtalmology and Visual Sciences, 41..
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Giese, M. A. (2000). Learning-based neural model for the recognition of biological motion patterns. Investigative Ophtalmology and Visual Sciences, 41.
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Giese, M. A. (1999). Evidence for Multi-functional Interactions in Early Visual Motion Processing,. Trends in Neurosciences, 22, 287-290.
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Poggio, T. A. & Giese, M. A. (1999). Synthesis and Recognition of Biological Motion Patterns Based on Linear Superposition of Prototypical Motion Sequences. In: Proceedings of the IEEE Workshop on Multi-View Modeling and Analysis of Visual Scene, Fort Collins, CO, June 21-23, 1999., 73-80.
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Dinse, H. R., Jancke, D., Akhavan, A. C., Kalt, T., Schöner, G. & Giese, M. A. (1999). Dynamic population representations of the visual and somatosensory cortex. Proceedings of the International Conference on Neural Information Processing 96. Springer, NY, 1285-1290.
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Poggio, T. A. & Giese, M. A. (1999). Recognition and synthesis of biological motion patterns by linear combination of prototypical motion patterns. In Elsner N, Eysel U (eds): Göttingen Neurobiology Report 1999. Thieme Verlag, Stuttgart, 501.
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Giese, M. A. (1998). Effiziente Methode zur Implementierung dynamischer neuronaler Felder. Patent No. 198 44 364.1. Deutsches Patentamt, München.
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Massberg, W., Weigt, D., Steinhage, A., Schöner, G. & Giese, M. A. (1998). Selbstorganisierende Fertigungssteuerung: Selbstorganisierendes Modell der Fertigungssteuerung auf der Basis neuronaler Dynamik. WT Werkstatt Technik, 7/8-98, 329-332.
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Giese, M. A. (1998). Dynamic neural field model links neural and computational theories of motion perception. Workshop Dynamische Perzeption. Infix Verlag, St. Augustin, Germany.
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Giese, M. A. (1998). A Dynamic Neural Field Model for the Perception of Apparent Motion. Phd Thesis.
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Cubaleska, B., Akhavan, A. C., Jancke, D., Dinse, H. R., Schöner, G. & Giese, M. A. (1998). Separation of feed-forward and recurrent parts of cortical dynamics in cat area 17. In Elsner N, Wehner R (eds): Göttingen Neurobiology Report 1998. Thieme Verlag, Stuttgart, 469.
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