A. Christensen, W. Ilg, H.-O. Karnath, M. A. Giese: Specific influences of self-motion on the detection of biological motion, Perception 38 ECVP Abstract Supplement, page 85, 2009

F. Fleischer, A. Casile, M. A. Giese: Invariant recognition of goal-directed hand actions: a physiologically plausible neural model, Perception 38 ECVP Abstract Supplement, page 51, 2009

M. A. Giese: Vision to action: Neurophysiology and modelling of the visual properties of mirror neurons, Perception 38 ECVP Abstract Supplement, page 2, 2009

M. A. Giese, V. Caggiagno and F. Fleischer: View-based neural encoding of goal-directed actions: a physiologically inspired neural theory, VSS 2010, Naples, FL 7-12 May 2010.

D. Endres, P. Földiák and U. Priss: An Application of Formal Concept Analysis to Semantic Neural Decoding, to appear in the Annals of Mathematics and Artificial Intelligence, special issue S47-CLA'08, 2010.

L. Omlor, New methods for anechoic demixing with application to shift invariant feature extraction, PhD Dissertation, Ulm University, Germany, 2010

D. M. Endres, M. A. Giese. Temporal Segmentation with Bayesian Binning. NIPS 2009 workshop on temporal segmentation, Whistler, BC 11-12 Dec. 2009.

D. M. Endres, A. Christensen, C. Beck, J. D. Bouecke, L. Omlor, H. Neumann, M. A. Giese: Segmentation of action streams: comparison between human and statistically optimal performance, VSS 2010, Naples, FL 7-12 May 2010.

D. M. Endres, A. Christensen, L. Omlor, M. A. Giese: Segmentation of action streams: human observers vs. Bayesian binning, submitted to NIPS 2010.

D. M. Endres, F. Vintila, N. Bruce, J. Bouecke, P. Kornprobst, H. Neumann, M. A. Giese: Hooligan detection: the effects of saliency and expert knowledge. To be presented at ECVP 2010.

D. M. Endres, U. Priss, P. Földiak: Interpreting the neural code with formal concept analysis. Perception 38 ECVP Abstract Supplement, page 127, 2009.

M. Chessa, A. Canessa, A. Gibaldi, F. Solari and S.P. Sabatini. Embedding Fixation Constraints into Binocular Energy-based Models of Depth Perception. International Conference on Cognitive and Neural Systems, Boston 27-30 May 2009.

Gibaldi, M. Chessa, A. Canessa, S.P. Sabatini and F. Solari. Reading binocular energy population codes for short-latency disparity-vergence eye movements. International Conference on Cognitive and Neural Systems, Boston 27-30 May 2009.

M. Chessa, F. Solari and S.P. Sabatini. A Virtual Reality Simulator for Active Stereo Vision Systems. International Conference on Computer Vision Theory and Applications, Lisbon 5-8 February 2009.

N. Bruce and P. Kornprobst. On the role of context in probabilistic models of visual saliency, Proceedings of the 2009 IEEE International Conference on Image Processing, 2009

N. D. B. Bruce and P. Kornprobst, Harris Corners in the Real World: A Principled Selection Criterion for Interest Points Based on Ecological Statistics, CVPR 2009

Raudies F, Neumann H (2010) A model of neural mechanisms in monocular transparent motion perception. J. of Physiol. - Paris 104, 7183.

Beck B, Neumann H (2010) Interactions of motion and form in visual cortex A neural model. J. of Physiol. - Paris 104, 6170

Hansen T and Neumann H: A recurrent model of contour integration in primary visual cortex. Journal of Vision 8(8): 8, 1-25, 2008.

Weidenbacher U and Neumann H: Extraction of surface-related features in a recurrent model of V1-V2 interactions. PLoS ONE 4(6): e5909, 2009.

Beck C., Ognibeni T., and Neumann H: Object segmentation from motion discontinuities and temporal occlusions - a biologically inspired model. PLoS ONE 3(11): e3807. doi:10.1371/journal.pone.0003807, 2008

E. Tlapale and G. S. Masson and P. Kornprobst, Motion Integration Modulated by Form Information, Conférence française de Neurosciences Computationnelles, Neurocomp08, 2008

E. Tlapale and G. S. Masson and P. Kornprobst, A neural model of luminance-gated recurrent motion diffusion for 2D motion integration and segmentation, INRIA Research report, 2009 (submitted to Cerebral Cortex)

M. Hoeffken, D. Oberhoff, and M. Kolesnik, Switching Markov Models for learning of motion patterns in videos, Artificial Neural Networks – ICANN, 2009