Section Computational
Sensomotorics
Department of Cognitive Neurology
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Year: 2008

Timmann, D., Brandauer, B., Hermsdörfer, J., Ilg, W., Konczak, J., Gerwig, M. et al. (2008). Lesion-Symptom Mapping of the Human Cerebellum. Cerebellum, 7(4), 602-6.
Lesion-Symptom Mapping of the Human Cerebellum
Authors: Timmann, Dagmar Brandauer, Barbara Hermsdörfer, J. Ilg, Winfried; Konczak, J\"urgen Gerwig, Marcus Gizewski, Elke R. Schoch, Beate
Research Areas: Uncategorized
Type of Publication: Article
Journal: Cerebellum
Volume: 7
Number: 4
Pages: 602-6
Year: 2008
Month: 01
Full text: PDF | Online version
Roether, C. L., Omlor, L. & Giese, M. A. (2008). Lateral asymmetry of bodily emotion expression. Current Biology, 18, R329-330.
Lateral asymmetry of bodily emotion expression
Authors: Roether, C. L. Omlor, Lars Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF | Online version
Park, A.-N., Mukovskiy, A., Omlor, L. & Giese, M. A. (2008). Synthesis of character behaviour by dynamic interaction of synergies learned from motion capture data. Skala V (ed): Proceedings of the 16th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG),4-7 Feb, Plzen, Czech Republic, 9-16.
Synthesis of character behaviour by dynamic interaction of synergies learned from motion capture data
Authors: Park, Aee-Ni Mukovskiy, Albert; Omlor, Lars Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Mukovskiy, A., Park, A.-N., Omlor, L., Slotine, J.-J. & Giese, M. A. (2008). Self-organization of character behavior by mixing of learned movement primitives. Proceedings of the 13th Fall Workshop on Vision, Modeling, and Visualization (VMV) , October 8-10, Konstanz, Germany, 121-130.
Self-organization of character behavior by mixing of learned movement primitives
Authors: Mukovskiy, Albert; Park, Aee-Ni Omlor, Lars Slotine, Jean-Jacques E. Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Ilg, W., Giese, M. A., Gizewski, E. R., Schoch, B. & Timmann, D. (2008). The influence of focal lesions of the cerebellum on the control and adaptation of gait. Brain, 131(Pt. 11), 2913-27.
The influence of focal lesions of the cerebellum on the control and adaptation of gait
Authors: Ilg, Winfried; Giese, Martin A.; Gizewski, Elke R. Schoch, Beate Timmann, Dagmar
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF | Online version
Giese, M. A., Thornton, I. & Edelman, S. (2008). Metrics of the perception of body movement. Journal of Vision, 8(9), 1-18.
Metrics of the perception of body movement
Authors: Giese, Martin A.; Thornton, Ian Edelman, Shimon
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Fleischer, F., Casile, A. & Giese, M. A. (2008). Neural Model for the Visual Recognition of Goal-directed Movements. In V. Kurkova, R. Neruda, and J. Koutnik (Eds.): Int Conf on Artifical Neural Networks (ICANN) 2008, Part II, LNCS, 5164, 939-948.
Neural Model for the Visual Recognition of Goal-directed Movements
Authors: Fleischer, Falk Casile, Antonino Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Fleischer, F., Casile, A. & Giese, M. A. (2008). Physiologically-inspired model for the visual tuning properties of mirror neurons. 3rd Int Conf on Cognitive Systems (CogSys) 2008, Karlsruhe, Germany, Springer Verlag, 19-24.
Physiologically-inspired model for the visual tuning properties of mirror neurons
Authors: Fleischer, Falk Casile, Antonino Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Curio, C., Giese, M. A., Breidt, M., Kleiner, M. & B\"ulthoff, H. H. (2008). Exploring human dynamic facial expression recognition with animation. Proceedings of the 2008 International Conference on Cognitive Systems, University of Karlsruhe, Karlsruhe, Germany, April 2-4, 2008, Springer Verlag.
Exploring human dynamic facial expression recognition with animation
Authors: Curio, Cristobal Giese, Martin A.; Breidt, Martin Kleiner, Mario B\"ulthoff, H. H.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Curio, C., Giese, M. A., Breidt, M., Kleiner, M. & B\"ulthoff, H. H. (2008). Probing Dynamic Human Facial Action Recognition From The Other Side Of The Mean. APGV '08: Proceedings of the 5th symposium on Applied perception in graphics and visualization, 59-66.
Probing Dynamic Human Facial Action Recognition From The Other Side Of The Mean
Authors: Curio, Cristobal Giese, Martin A.; Breidt, Martin Kleiner, Mario B\"ulthoff, H. H.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF | Online version
B\"ulthoff, H. H., Wallraven, C. & Giese, M. A. (2008). Perceptual Robotics: Example-based representations of shapes and movements. In Siciliano B, Khatib O: Springer Handbook of Robotics, 1481-1498.
Perceptual Robotics: Example-based representations of shapes and movements
Authors: B\"ulthoff, H. H. Wallraven, Christian Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Omlor, L., Giese, M. A. & Roether, C. L (2008). Distinctive postural and dynamic features for bodily emotion expression Journal of Vision, 8(6), 910a.
Distinctive postural and dynamic features for bodily emotion expression
Authors: Omlor, Lars Giese, Martin A.; Roether, C. L.
Research Areas: Uncategorized
Type of Publication: In Collection
Month: 01
Pages: 910a
Full text: Online version
Fleischer, F., Casile, A. & Giese, M. A (2008). Neural model for the recognition of transitive actions Perception, 37(suppl.), 155.
Neural model for the recognition of transitive actions
Abstract:

Neural model for the recognition of transitive actions The visual recognition of goal-directed movements is crucial for imitation and possibly the understanding of actions.We present a neurophysiologically-inspired model for the recognition of goal-directed hand movements. The model exploits neural principles that have been used before to account for object and action recognition: (i) hierarchical neural architecture extracting form and motion features; (ii) optimization of mid-level features by learning; (iii) realization of temporal sequence selectivity by recurrent neural circuits. Beyond these classical principles, the model proposes novel physiologically plausible mechanisms for the integration of information about effector shape, motion, goal object, and affordance. We demonstrate that the model is powerful enough to recognize hand actions from real video sequences and reproduces charac- teristic properties of real cortical neurons involved in action recognition. We conclude that: (i) goal-directed actions can be recognized by view-based mechanisms without a simulation of the actions in 3-D, (ii) well-established neural principles of object and motion recognition are sufficient to account for the visual recognition of goal-directed transitive actions.

Authors: Fleischer, Falk Casile, Antonino Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
Full text: Online version
Fleischer, F., Casile, A. & Giese, M. A (2008). Simulating mirror-neuron responses using a neural model for visual action recognition Proceedings of the Seventeenth Annual Computational Neuroscience Meeting CNS, July 19th - 24th 2008, Portland, Oregon, USA.
Simulating mirror-neuron responses using a neural model for visual action recognition
Abstract:

Simulating mirror-neuron responses using a neural model for visual action recognition The visual recognition of goal-directed movements is crucial for the learning of actions, and possibly for the understanding of the intentions and goals of others. The discovery of mirror neurons has stimulated a vast amount of research investigating possible links between action perception and action execution [1,2]. However, it remains largely unknown what is the real extent of this putative visuo-motor interaction during visual perception of actions and which relevant computational functions are instead accomplished by possibly purely visual processing. We present a neurophysiologically inspired model for the visual recognition of hand movements. It demonstrates that several experimentally shown properties of mirror neurons can be explained by the analysis of spatio-temporal visual features within a hierarchical neural system that reproduces fundamental properties of the visual pathway and premotor cortex. The model integrates several physiologically plausible computational mechanisms within a common architecture that is suitable for the recognition of grasping actions from real videos: (1) A hierarchical neural architecture that extracts 2D form features with subsequently increasing complexity and invariance to position along the hierarchy [3,4,5]. (2) Extraction of optimal features on different hierarchy levels by eliminating features which are not contributing to correct classification results. (3) Simple recurrent neural circuits for the realization of temporal sequence selectivity [6,7,8]. (4) A simple neural mechanism that combines the spatial information about goal object and its affordance and the information about the end effector and its movement. The model is validated with video sequences of both monkey and human grasping actions. We show that simple well-established physiologically plausible mechanisms can account for important aspects of visual action recognition and experimental data of the mirror neuron system. Specifically, these results are independent of explicit 3D representations of objects and the action. Instead, it realizes predictions over time based on learned 2D pattern sequences arising in the visual input. Our results complements those of existing models [9] and motivates a more detailed analysis of the complementary contributions of visual pattern analysis and motor representations on the visual recognition of imitable actions. References 1. di Pellegrino G, Fadiga L, Fogassi L, Gallese V, Rizzolatti G: Understanding motor events: a neurophysiological study. Exp Brain Res 1992, 91:176-180. 2. Rizzolatti G, Craighero L: The mirror-neuron system. Annu Rev Neurosci 2004, 27:169-192. 3. Giese MA, Poggio T: Neural mechanisms for the recognition of biological movements. Nat Rev Neurosci 2003, 4:179-192. 4. Riesenhuber M, Poggio T: Hierarchical models of object recognition in cortex. Nat Neurosci 1999, 2:1019-1025. 5. Serre T, Wolf L, Bileschi S, Riesenhuber M, Poggio T: Robust object recognition with cortex-like mechanisms. IEEE Trans Pattern Anal Mach Intell 2007, 29:411-426. 6. Xie X, Giese MA: Nonlinear dynamics of direction-selective recurrent neural media. Phys Rev E Stat Nonlin Soft Matter Phys 2002, 65:051904. 7. Zhang K: Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory. J Neurosci 1996, 16:2112-2126. 8. Hopfield JJ, Brody CD: What is a moment? "Cortical" sensory integration over a brief interval. Proc Natl Acad Sci U S A 2000, 97:13919-13924. 9. Oztop E, Kawato M, Arbib M: Mirror neurons and imitation: a computationally guided review. Neural Netw 2006, 19:254-271.

Authors: Fleischer, Falk Casile, Antonino Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
Fleischer, F., Casile, A. & Giese, M. A (2008). Neural model for the visual recognition of actions Int Conf on Cognitive Systems Neuroscience (COSYNE) 2008, Salt Lake City, USA.
Neural model for the visual recognition of actions
Abstract:

Neural model for the visual recognition of actions The visual recognition of goal-directed movements is crucial for the learning of actions, and possibly for the understanding of the intentions and goals of others. The discovery of mirror neurons has stimulated a vast amount of research investigating possible links between action perception and action execution [1,2]. However, it remains largely unknown what is the real extent of this putative visuo-motor interaction during visual perception of actions and which relevant computational functions are instead accomplished by possibly purely visual processing. Here, we present a neurophysiologically inspired model for the recognition of hand movements demonstrating that a substantial degree of performance can be accomplished by the analysis of spatio-temporal visual features within a hierarchical neural system that reproduces fundamental properties of the visual pathway and premotor cortex. The model integrates several physiologically plausible computational mechanisms within a common architecture that is suitable for the recognition of grasping actions from real videos: (1) A hierarchical neural architecture that extracts form and motion features with position and scale invariance by subsequent increase of feature complexity and invariance along the hierarchy [3,4,5]. (2) Learning of optimized features on different hierarchy levels using a trace learning rule that eliminates features which are not contributing to correct classification results [5]. (3) Simple recurrent neural circuits for the realization of temporal sequence selectivity [6,7,8]. (4) As novel computational function the model implements a plausible mechanism that combines the spatial information about goal object and its affordance and the specific posture, position and orientation of the effector (hand). The model is evaluated on video sequences of both monkey and human grasping actions. The model demonstrates that simple well-established physiologically plausible mechanisms account for important aspects of visual action recognition. Specifically, the proposed model does not contain explicit 3D representations of objects and the action. Instead, it realizes predictions over time based on learned pattern sequences arising in the visual input. Our results complements those of existing models [9] and motivates a more detailed analysis of the complementary contributions of visual pattern analysis and motor representations on the visual recognition of imitable actions. References [1] Di Pellegrino, G. et al. (1992): Exp. Brain Res. 91, 176-180. [2] Rizzolatti, G. and Craighero, L. (2004): Annu. Rev. Neurosci. 27, 169-192. [3] Riesenhuber, M. and Poggio, T. (1999): Nat. Neurosci. 2, 1019-1025. [4] Giese, A.M. and Poggio, T. (2003): Nat. Rev. Neurosci. 4, 179-192. [5] Serre, T. et al. (2007): IEEE Pattern Anal. Mach. Int. 29, 411-426. [6] Zhang, K. (1996): J. Neurosci. 16, 2112-2126. [7] Hopfield, J. and Brody, D. (2000): Proc Natl Acad Sci USA 97, 13919-13924. [8] Xie, X. and Giese, M. (2002): Phys Rev E Stat Non

Authors: Fleischer, Falk Casile, Antonino Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
Ilg, W., Christensen, A., Karnath, H. O. & Giese, M. A (2008). Facilitation of action recognition by self-generated movements depends critically on timing Neuroscience Meeting, Washington DC.
Facilitation of action recognition by self-generated movements depends critically on timing
Authors: Ilg, Winfried; Christensen, Andrea Karnath, H. O. Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
Fleischer, F., Casile, A. & Giese, M. A (2008). Neural model for the visual recognition of hand actions Journal of Vision, 8(6), 53a.
Neural model for the visual recognition of hand actions
Abstract:

Neural model for the visual recognition of actions The visual recognition of goal-directed movements is crucial for the learning of actions, and possibly for the understanding of the intentions and goals of others. The discovery of mirror neurons has stimulated a vast amount of research investigating possible links between action perception and action execution [1,2]. However, it remains largely unknown what is the real extent of this putative visuo-motor interaction during visual perception of actions and which relevant computational functions are instead accomplished by possibly purely visual processing. Here, we present a neurophysiologically inspired model for the recognition of hand movements demonstrating that a substantial degree of performance can be accomplished by the analysis of spatio-temporal visual features within a hierarchical neural system that reproduces fundamental properties of the visual pathway and premotor cortex. The model integrates several physiologically plausible computational mechanisms within a common architecture that is suitable for the recognition of grasping actions from real videos: (1) A hierarchical neural architecture that extracts form and motion features with position and scale invariance by subsequent increase of feature complexity and invariance along the hierarchy [3,4,5]. (2) Learning of optimized features on different hierarchy levels using a trace learning rule that eliminates features which are not contributing to correct classification results [5]. (3) Simple recurrent neural circuits for the realization of temporal sequence selectivity [6,7,8]. (4) As novel computational function the model implements a plausible mechanism that combines the spatial information about goal object and its affordance and the specific posture, position and orientation of the effector (hand). The model is evaluated on video sequences of both monkey and human grasping actions. The model demonstrates that simple well-established physiologically plausible mechanisms account for important aspects of visual action recognition. Specifically, the proposed model does not contain explicit 3D representations of objects and the action. Instead, it realizes predictions over time based on learned pattern sequences arising in the visual input. Our results complements those of existing models [9] and motivates a more detailed analysis of the complementary contributions of visual pattern analysis and motor representations on the visual recognition of imitable actions. References [1] Di Pellegrino, G. et al. (1992): Exp. Brain Res. 91, 176-180. [2] Rizzolatti, G. and Craighero, L. (2004): Annu. Rev. Neurosci. 27, 169-192. [3] Riesenhuber, M. and Poggio, T. (1999): Nat. Neurosci. 2, 1019-1025. [4] Giese, A.M. and Poggio, T. (2003): Nat. Rev. Neurosci. 4, 179-192. [5] Serre, T. et al. (2007): IEEE Pattern Anal. Mach. Int. 29, 411-426. [6] Zhang, K. (1996): J. Neurosci. 16, 2112-2126. [7] Hopfield, J. and Brody, D. (2000): Proc Natl Acad Sci USA 97, 13919-13924. [8] Xie, X. and Giese, M. (2002): Phys Rev E Stat Nonlin Soft Matter Phys 65, 051904. [9] Oztop, E. et al. (2006): Neural Netw. 19, 254-271.

Authors: Fleischer, Falk Casile, Antonino Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
Park, A.-N., Mukovskiy, A., Omlor, L. & Giese, M. A. (2008). Self organized character animation based on learned synergies from full-body motion capture data. Proceedings of the 2008 International Conference on Cognitive Systems (CogSys), University of Karlsruhe, Karlsruhe, Germany, 2-4 April, Springer-Verlag, Berlin.
Self organized character animation based on learned synergies from full-body motion capture data
Authors: Park, Aee-Ni Mukovskiy, Albert; Omlor, Lars Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF | Online version
Endres, D. & Földiák, P. (2008). Exact Bayesian Bin Classification: A Fast Alternative to Bayesian Classification and its Application to Neural Response Analysis. Journal of Computational Neuroscience, 24(1), 24-35.
Exact Bayesian Bin Classification: A Fast Alternative to Bayesian Classification and its Application to Neural Response Analysis
Authors: Endres, Dominik Földiák, Peter
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Földiák, P. & Endres, D. (2008). Sparse Coding. Scholarpedia, 3(1), 2984.
Sparse Coding
Authors: Földiák, Peter Endres, Dominik
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version

Year: 2007

Giese, M. A. (2007). Learnimng-based representations of complex body movements: Studies in brains and machines. Phd Thesis.
Learnimng-based representations of complex body movements: Studies in brains and machines
Authors: Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Phd Thesis
Month: 01
Full text: Online version
Ilg, W., Röhrig, R., Thier, P. & Giese, M. A. (2007). Learning-based methods for the analysis of intra-limb coordination and adaptation of locomotor patterns in cerebellar patients. IEEE 10th International Conference on Rehabilitation Robotics, 13-15 June, Noordwijk, The Netherlands, pages: 1090-1095.
Learning-based methods for the analysis of intra-limb coordination and adaptation of locomotor patterns in cerebellar patients
Authors: Ilg, Winfried; Röhrig, R. Thier, Peter Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Broetz, D., Burkard, S., Schöls, L., Synofzik, M. & Ilg, W. (2007). Koordination im Mittelpunkt - Physiotherapiekonzept bei zerebellärer Ataxie. Physiopraxis, 5(11/12), 23-26.
Koordination im Mittelpunkt - Physiotherapiekonzept bei zerebellärer Ataxie
Authors: Broetz, D. Burkard, Susanne Schöls, L. Synofzik, Matthis Ilg, Winfried
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Omlor, L. & Giese, M. A. (2007). Learning of translation-invariant independent components: multivariate anechoic mixtures. MultiLearning of Translation-Invariant Independent Components: Multivariate Anechoic Mixtures. In: Davies M.E., James C.J., Abdallah S.A., Plumbley M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007., 4666, 762-769.
Learning of translation-invariant independent components: multivariate anechoic mixtures
Authors: Omlor, Lars Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Omlor, L. & Giese, M. A. (2007). Extraction of spatio-temporal primitives of emotional body expressions. Neurocomputing, 70(10-12), 1938-1942.
Extraction of spatio-temporal primitives of emotional body expressions
Authors: Omlor, Lars Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF | Online version
Graf, M., Reitzner, B., Corves, C., Casile, A., Giese, M. A. & Prinz, W. (2007). Predicting point-light actions in real-time. Neuroimage, 36(suppl. 2), T22-23.
Predicting point-light actions in real-time
Authors: Graf, Markus Reitzner, Bianca Corves, Caroline Casile, Antonino Giese, Martin A.; Prinz, Wolfgang
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF | Online version
Dayan, E., Casile, A., Levit-Binnun, N., Giese, M. A., Hendler, T. & Flash, T. (2007). Neural representations of kinematic laws of motion: evidence for action-perception coupling. Proceedings of the National Academy of Sciences (PNAS), 104(51), 20582-20587.
Neural representations of kinematic laws of motion: evidence for action-perception coupling
Authors: Dayan, Eran Casile, Antonino Levit-Binnun, Nava Giese, Martin A.; Hendler, Talma Flash, Tamar
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF | Online version
Giese, M. A. & Leopold, D. A. (2007). Wie wir Gesichter erkennen. Spektrum der Wissenschaft, 3/7, 20-23.
Wie wir Gesichter erkennen
Authors: Giese, Martin A.; Leopold, David A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF | Online version
Ilg, W., Golla, H., Thier, P. & Giese, M. A. (2007). Specific influences of cerebellar dysfunctions on gait. Brain, 130, 786-798.
Specific influences of cerebellar dysfunctions on gait
Authors: Ilg, Winfried; Golla, Heidrun Thier, Peter Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF | Online version
Thier, P., Caggiano, V., Fogassi, L., Rizzolatti, G., Casile, A. & Giese, M. A. (2007). Differential encoding of actions in near and far space in the mirror neuron system of monkeys. Proc. of 37th Annual Meeting of the Society for Neuroscience, 3rd-7th November 2007, San Diego (USA).
Differential encoding of actions in near and far space in the mirror neuron system of monkeys
Authors: Thier, Peter Caggiano, Vittorio Fogassi, Leonardo Rizzolatti, Giacomo Casile, Antonino Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Serre, T. & Giese, M. A. (2007). Rapid Serial Action Presentation: New paradigm for the study of movement recognition. Journal of Vision, 7(9), 559a.
Rapid Serial Action Presentation: New paradigm for the study of movement recognition
Authors: Serre, Tomas Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Roether, C. L., Omlor, L. & Giese, M. A. (2007). Not just the face: asymmetry of emotional body expression. Journal of Vision, 7(9), 554a.
Not just the face: asymmetry of emotional body expression
Authors: Roether, C. L. Omlor, Lars Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Omlor, L., Giese, M. A. & Roether, C. L. (2007). Left-right asymmetry of emotionally expressive full-body movement. B\"ulthoff H H, Chatziastros A, Mallot H A, Ulrich R (eds): Proceedings of the 10th. T\"ubinger Perception Conference (TWK 2007), Knirsch, Kirchentellinsfurt, 152.
Left-right asymmetry of emotionally expressive full-body movement
Authors: Omlor, Lars Giese, Martin A.; Roether, C. L.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Roether, C. L., Omlor, L. & Giese, M. A. (2007). Asymmetry of emotions expressed in full-body movement. Perception, 36(suppl.), 75.
Asymmetry of emotions expressed in full-body movement
Authors: Roether, Claiere L. Omlor, Lars Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Park, A.-N., Omlor, L. & Giese, M. A. (2007). Synergy-based method for the self-organization of full-body movements with high degree of realism. B\"ulthoff H H, Chatziastros A, Mallot H A, Ulrich R (eds): Proceedings of the 10th. T\"ubinger Perception Conference (TWK 2007), Knirsch, Kirchentellinsfurt, 152.
Synergy-based method for the self-organization of full-body movements with high degree of realism
Authors: Park, Aee-Ni Omlor, Lars Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Mukovskiy, A. & Giese, M. A. (2007). Style synthesis of human body motion based on learned spatio-temporal synergies. B\"ulthoff H H, Chatziastros A, Mallot H A, Ulrich R (eds): Proceedings of the 10th. T\"ubinger Perception Conference (TWK 2007), Knirsch, Kirchentellinsfurt, 152.
Style synthesis of human body motion based on learned spatio-temporal synergies
Research Areas: Uncategorized
Type of Publication: Article
Giese, M. A., Caggiano, V., Fogassi, L., Rizzolatti, G., Thier, P. & Casile, A. (2007). Mirror neurons encoding the expectation of a reward. Proceedings of 37th Annual Meeting of the Society for Neuroscience , 3-7 November, San Diego (USA).
Mirror neurons encoding the expectation of a reward
Authors: Giese, Martin A.; Caggiano, Vittorio Fogassi, Leonardo Rizzolatti, Giacomo Thier, Peter Casile, Antonino
Research Areas: Uncategorized
Type of Publication: Article
Fleischer, F., Casile, A. & Giese, M. A. (2007). Neural model for the visual recognition of goal-directed actions. ESF-EMBO Symposium: Three Dimensional Sensory and Motor Space: Perceptual Consequences of Motor Action , 6-11 October, Sant Feliu de Guixols, Spain.
Neural model for the visual recognition of goal-directed actions
Abstract:

Neural model for the visual recognition of goal-directed actions The visual recognition of goal-directed movements is crucial for the learning of actions, and possibly for the understanding of the intentions and goals of others. The discovery of mirror neurons has stimulated a vast amount of research investigating possible links between action perception and action execution [1,2]. However, it remains largely unknown what the precise nature this visuo-motor interaction is, and which relevant computational functions can be accomplished by purely visual processing. We present a neurophysiologically inspired model for the recognition of hand movements demonstrating that a substantial degree of action understanding can be accomplished by appropriate analysis of spatio-temporal visual features. The model is based on a hierarchical feed-forward architecture for invariant object and motion recognition [3,4,5] employing principles that are similar to the ones that have been established for stationary object recognition. The model addresses in particular how invariance against position variations of object and effector can be accomplished, while preserving the relative spatial information that is required for an accurate recognition of the hand-object interaction. It is demonstrated that the model is able to correctly classify different grasp types determining whether the action matches correctly the object affordance. The model demonstrates that well-established simple physiologically plausible neural mechanisms account for important aspects of visual action recognition without the need of a detailed 3D representation of object and action. It complements existing models and provides a basis for a further quantitative analysis of visual influences on action recognition. [1] Di Pellegrino, G. et al. (1992): Exp. Brain Res. 91, 176-180. [2] Rizzolatti, G. and Craighero, L. (2004): Annu. Rev. Neurosci. 27, 169-192. [3] Riesenhuber, M. and Poggio, T. (1999): Nat. Neurosci. 2, 1019-1025. [4] Giese, A.M. and Poggio, T. (2003): Nat. Rev. Neurosci. 4, 179-192. [5] Serre, T. et al. (2007): IEEE Pattern Anal. Mach. Int. 29, 411-426.

Authors: Fleischer, Falk Casile, Antonino Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Fleischer, F. & Giese, M. A. (2007). Neural model for the visual recognition of goal-directed hand movements. B\"ulthoff H H, Chatziastros A, Mallot H A, Ulrich R (eds): Proceedings of the 10th. T\"ubinger Perception Conference (TWK 2007), Knirsch, Kirchentellinsfurt, 152.
Neural model for the visual recognition of goal-directed hand movements
Abstract:

Neural model for the visual recognition of goal-directed hand movements The visual recognition of goal-directed movements is crucial for the learning of actions, and possibly for understanding the intentions and goals of others. The discovery of mirror neurons has stimulated a vast amount of research investigating possible links between action perception and action execution [1,2,3]. However, the neural mechanisms underlying the visual recognition of goal-directed movements remain largely unclear. One class of theories suggests, that action recognition is mainly based on a covert internal re-simulation of executed motor acts, potentially even in a joint coordinate system. Another set of approaches assumes that a substantial degree of action understanding might be accomplished by appropriate analysis of spatio-temporal visual features, employing mechanisms that meanwhile are largely accepted as basis for the recognition of non-moving stationary objects. We present a neurophysiologically inspired model for the recognition of hand movements that demonstrates the feasibility of the second approach, recognizing hand actions from real video data. The model addresses in particular how invariance against position variations of object and effector can be accomplished, while preserving the relative spatial information that is required for an accurate recognition of the hand-object interaction. The model is based on a hierarchical feed-forward architecture for invariant object and motion recognition [4,5]. It extends previous approaches to complex stimuli like hands, and adds the capability for the processing of position information. The ability to recognize objects relies on a dictionary of shape-selective cells that are learned in an unsupervised manner from natural images. Feature complexity and invariance properties increase along the hierarchy by linear and nonlinear pooling operations. It is demonstrated that the model is able to correctly classify different grasp types and is suitable for determining the spatial relationships between effector and object, which are crucial for determining whether the action matches correctly the object affordance. The model demonstrates that well-established simple physiologically plausible neural mechanisms account for important aspects of visual action recognition without the need of a detailed 3D representation of object and action. This seems important since the robust extraction of joint angles from videos is a hard and largely unresolved computational problem, for which so far no physiologically plausible neural models have been proposed. [1] di Pellegrino, G. et al. (1992): Exp. Brain Res. 91, 176-180 [2] Gallese, V. et al. (1996): Brain 119, 593-609 [3] Rizzolatti, G. and Craighero, L. (2004): Annu. Rev. Neurosci. 27, 169-192 [4] Riesenhuber, M. and Poggio, T. (1999): Nat. Neurosci. 2, 1019-1025 [5] Giese, A.M. and Poggio, T. (2003): Nat. Rev. Neurosci. 4, 179-192

Authors: Fleischer, Falk Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Curio, C., Breidt, M., Kleiner, M., B\, H. H. & Giese, M. A (2007). High-level after-effects in the recognition of dynamic facial expressions. Journal of Vision Perception, 36(suppl.), 994.
High-level after-effects in the recognition of dynamic facial expressions
Authors: Curio, Cristobal Breidt, Martin Kleiner, Mario B\, H. H. Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
Curio, C., Breidt, M., Kleiner, M., B\"ulthoff, H. H. & Giese, M. A. (2007). High-level after-effects in the recognition of dynamic facial expressions. Journal of Vision, 7(9), 994.
High-level after-effects in the recognition of dynamic facial expressions
Authors: Curio, Cristobal Breidt, Martin Kleiner, Mario B\"ulthoff, H. H. Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
Caggiano, V., Fogassi, L., Rizzolatti, G., Thier, P., Casile, A. & Giese, M. A (2007). Neurons in monkey pre-motor cortex (area F5) responding to filmed actions Perception, 36(suppl.), 73.
Neurons in monkey pre-motor cortex (area F5) responding to filmed actions
Authors: Caggiano, Vittorio Fogassi, Leonardo Rizzolatti, Giacomo Thier, Peter Casile, Antonino Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
Full text: Online version
Caggiano, V., Fogassi, L., Rizzolatti, G., Thier, P., Casile, A. & Giese, M. A (2007). Mirror neurons responding to filmed actions Proc. of 37th Annual Meeting of the Society for Neuroscience , 3rd-7th November 2007, San Diego (USA)..
Mirror neurons responding to filmed actions
Authors: Caggiano, Vittorio Fogassi, Leonardo Rizzolatti, Giacomo Thier, Peter Casile, Antonino Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
Full text: Online version
Curio, C., Breidt, M., Kleiner, M., B\"ulthoff, H. H. & Giese, M. A. (2007). Perception of dynamic facial expressions probed by a new high-level after-effect. B\"ulthoff H H, Chatziastros A, Mallot H A, Ulrich R (eds): Proceedings of the 10th. T\"ubinger Perception Conference (TWK 2007), Knirsch, Kirchentellinsfurt, 152.
Perception of dynamic facial expressions probed by a new high-level after-effect
Authors: Curio, Cristobal Breidt, Martin Kleiner, Mario B\"ulthoff, H. H. Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version

Year: 2006

Ilg, W., Golla, H. & Giese, M. A. (2006). Velocity-dependent stability of gait for patients with balance impairments can be explained by biomechanical stabilization. XVIIth Conference of the International Society for Postural and Gait Research, 24(2), S113-S114.
Velocity-dependent stability of gait for patients with balance impairments can be explained by biomechanical stabilization
Type of Publication: Article
Full text: PDF | Online version
Jastorff, J., Kourtzi, Z. & Giese, M. A. (2006). Learning to discriminate complex movements: Natural vs artificial trajectories. Journal of Vision, 6(8), 791-804.
Learning to discriminate complex movements: Natural vs artificial trajectories
Type of Publication: Article
Full text: Online version
Leopold, D. A., Bondar, I. V. & Giese, M. A. (2006). Norm-based face encoding by single neurons in the monkey inferotemporal cortex. Nature, 442(7102), 572-575.
Norm-based face encoding by single neurons in the monkey inferotemporal cortex
Authors: Leopold, David A. Bondar, Igor V. Giese, Martin A.
Type of Publication: Article
Full text: Online version
Omlor, L. & Giese, M. A. (2006). Unsupervised learning of spatio-temporal primitives of emotional gait. Perception and Interactive Technologies 2006, Lecture Notes in Artificial Intelligence, 4021, 188-192.
Unsupervised learning of spatio-temporal primitives of emotional gait
Type of Publication: Article
Full text: Online version
Casile, A. & Rucci, M. (2006). A theoretical analysis of the influence of fixational instability on the development of thalamocortical connectivity. Neural Computation, 18(3), 569-590.
A theoretical analysis of the influence of fixational instability on the development of thalamocortical connectivity
Authors: Casile, Antonino Rucci, Michele
Type of Publication: Article
Full text: Online version
Casile, A. & Giese, M. A. (2006). Non-visual motor learning influences the recognition of biological motion. Current Biology, 16(1), 69-74.
Non-visual motor learning influences the recognition of biological motion
Type of Publication: Article
Full text: Online version

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