Personal Page

Dr. Mukovskiy, Albert

Room: 4.534
Section for Computational Sensomotorics
Department of Cognitive Neurology
Hertie Institute for Clinical Brain Research
Centre for Integrative Neuroscience
University Clinic Tübingen
Otfried-Müller-Str. 25
72076 Tübingen, Germany
+497071 2989224
albert.mukovskiy@medizin.uni-tuebingen.de
Albert Mukovskiy

Projects

Publications

Marrazzo, G., Martino, F. D., Mukovskiy, A., Giese, M. A. & de Gelder, B. (2025). Neural encoding of biomechanically (im)possible human movements in occipitotemporal cortex. bioRxiv.
Neural encoding of biomechanically (im)possible human movements in occipitotemporal cortex
Abstract:

Understanding how the human brain processes body movements is essential for clarifying the mechanisms underlying social cognition and interaction. This study investigates the encoding of biomechanically possible and impossible body movements in occipitotemporal cortex using ultra-high field 7Tesla fMRI. By predicting the response of single voxels to impossible/possible movements using a computational modelling approach, our findings demonstrate that a combination of postural, biomechanical, and categorical features significantly predicts neural responses in the ventral visual cortex, particularly within the extrastriate body area (EBA), underscoring the brain{\textquoteright}s sensitivity to biomechanical plausibility. Lastly, these findings highlight the functional heterogeneity of EBA, with specific regions (middle/superior occipital gyri) focusing on detailed biomechanical features and anterior regions (lateral occipital sulcus and inferior temporal gyrus) integrating more abstract, categorical information.Competing Interest StatementThe authors have declared no competing interest.

Authors: Marrazzo, Giuseppe Martino, Federico De Mukovskiy, Albert; Giese, Martin A.; de Gelder, Beatrice
Type of Publication: Article
Journal: bioRxiv
Year: 2025
Full text: PDF Online version
[Bibtex]
Lappe, A., Bognár, A., Nejad, G. G., Raman, R., Mukovskiy, A., Martini, L. M. et al (2024). Predictive Features in Deep Neural Network Models of Macaque Body Patch Selectivity. Journal of Vision September 2024 . Vision Science Society.
Predictive Features in Deep Neural Network Models of Macaque Body Patch Selectivity
Abstract:

Previous work has shown that neurons from body patches in macaque superior temporal sulcus (STS) respond selectively to images of bodies. However, the visual features leading to this body selectivity remain unclear. METHODS: We conducted experiments using 720 stimuli presenting a monkey avatar in various poses and viewpoints. Spiking activity was recorded from mid-STS (MSB) and anterior-STS (ASB) body patches, previously identified using fMRI. To identify visual features driving the neural responses, we used a model with a deep network as frontend and a linear readout model that was fitted to predict the neuron activities. Computing the gradients of the outputs backwards along the neural network, we identified the image regions that were most influential for the model neuron output. Since previous work suggests that neurons from this area also respond to some extent to images of objects, we used a similar approach to visualize object parts eliciting responses from the model neurons. Based on an object dataset, we identified the shapes that activate each model unit maximally. Computing and combining the pixel-wise gradients of model activations from object and body processing, we were able to identify common visual features driving neural activity in the model. RESULTS: Linear models fit the data well, with mean noise-corrected correlations with neural data of 0.8 in ASB and 0.94 in MSB. Gradient analysis on the body stimuli did not reveal clear preferences of certain body parts and were difficult to interpret visually. However, the joint gradients between objects and bodies traced visually similar features in both images. CONCLUSION: Deep neural networks model STS data well, even though for all tested models, explained variance was substantially lower in the more anterior region. Further work will test if the features that the deep network relies on are also used by body patch neurons.

Authors: Lappe, Alexander; Bognár, Anna Nejad, Ghazaleh Ghamkhari Raman, Rajani Mukovskiy, Albert; Martini, Lucas M.; Vogels, Rufin Giese, Martin A.
Type of Publication: In Collection
JRESEARCH_BOOK_TITLE: Journal of Vision September 2024
Publisher: Vision Science Society
Month: September
[Bibtex]
Lappe, A., Bognár, A., Nejad, G. G., Mukovskiy, A., Martini, L. M., Giese, M. A. et al. (2024). Parallel Backpropagation for Shared-Feature Visualization. Advances in Neural Information Processing Systems(37), 22993-23012.
Parallel Backpropagation for Shared-Feature Visualization
Authors: Lappe, Alexander; Bognár, Anna Nejad, Ghazaleh Ghamkhari Mukovskiy, Albert; Martini, Lucas M.; Giese, Martin A.; Vogels, Rufin
Type of Publication: Article
Full text: PDF Online version
[Bibtex]
Abassi, E., Bognár, A., de Gelder, B., Giese, M. A., Isik, L., Lappe, A. et al. (2024). Neural Encoding of Bodies for Primate Social Perception. Journal of Neuroscience, 44(40).
Neural Encoding of Bodies for Primate Social Perception
Abstract:

Primates, as social beings, have evolved complex brain mechanisms to navigate intricate social environments. This review explores the neural bases of body perception in both human and nonhuman primates, emphasizing the processing of social signals conveyed by body postures, movements, and interactions. Early studies identified selective neural responses to body stimuli in macaques, particularly within and ventral to the superior temporal sulcus (STS). These regions, known as body patches, represent visual features that are present in bodies but do not appear to be semantic body detectors. They provide information about posture and viewpoint of the body. Recent research using dynamic stimuli has expanded the understanding of the body-selective network, highlighting its complexity and the interplay between static and dynamic processing. In humans, body-selective areas such as the extrastriate body area (EBA) and fusiform body area (FBA) have been implicated in the perception of bodies and their interactions. Moreover, studies on social interactions reveal that regions in the human STS are also tuned to the perception of dyadic interactions, suggesting a specialized social lateral pathway. Computational work developed models of body recognition and social interaction, providing insights into the underlying neural mechanisms. Despite advances, significant gaps remain in understanding the neural mechanisms of body perception and social interaction. Overall, this review underscores the importance of integrating findings across species to comprehensively understand the neural foundations of body perception and the interaction between computational modeling and neural recording.

Authors: Abassi, Etienne Bognár, Anna de Gelder, Bea Giese, Martin A.; Isik, Leyla Lappe, Alexander; Mukovskiy, Albert; Solanas, Marta Poyo Taubert, Jessica Vogels, Rufin
Type of Publication: Article
Full text: Online version
[Bibtex]
Lappe, A., Bognár, A., Nejad, G. G., Mukovskiy, A., Giese, M. A. & Vogels, R (2024). Encoding of bodies and objects in body-selective neurons. Society for Neuroscience .
Encoding of bodies and objects in body-selective neurons
Abstract:

The primate visual system has evolved subareas in which neurons appear to respond more strongly to images of a specific semantic category, like faces or bodies. The computational processes underlying these regions remain unclear, and there is debate on whether this effect is in fact driven by semantics or rather by visual features that occur more often among images from the specific category. Recent works tackling the question of whether the same visual features drive responses of face-selective cells to face images and non-face images have yielded mixed results. Here, we report findings on shared encoding of body and object images in body-selective neurons in macaque superior temporal sulcus. We targeted two fMRI-defined regions, anterior and posterior body patches in two awake macaques using V probes, recording multi-unit activity in and around these patches. In a first phase, we recorded responses to a set of 475 images of a macaque avatar in various poses. We then trained a deep-neural-network based model to predict responses to these images, and subsequently evaluated the model on two sets of object and body stimuli consisting of 6857 and 2068 images, respectively. These images comprised a variety of object types and animal species. After the inference process, we selected the highest and lowest predicted activator for each recording channel from both object and body images. In a second phase, we recorded responses of the same multi-units to these stimuli. For analysis, we only kept those multi-unit sites with high test/retest reliability. Also, we only considered multi-unit sites for which the selected bodies elicited a significantly higher response than the selected objects. We then tested whether the high-predicted objects/bodies indeed lead to higher responses at the corresponding electrode than the low-predicted ones. Across neurons, we found a significant preference of the high-predicted stimulus for both objects and bodies. The highly-activating objects consisted of a variety of everyday objects and did not necessarily globally resemble a body. Furthermore, the correlations between predicted and recorded responses to the objects were consistently positive for both monkeys and recording areas, meaning that the model was able to predict responses to objects after having only been trained on images of a macaque avatar. Our results show that the feature preferences of body-selective neurons are at least partially shared between bodies and objects. On a larger scope, we provide further evidence that category selectivity arises due to highly shared visual features among category instances, rather than semantics.

Authors: Lappe, Alexander; Bognár, A. Nejad, G. G. Mukovskiy, Albert; Giese, Martin A.; Vogels, Rufin
Research Areas: Uncategorized
Type of Publication: In Collection
[Bibtex]
Vogels, R., Raman, R., Nejad, G. G., Mukovskiy, A., Lappe, A., Giese, M. A. et al (2024). Keypoint-based modeling of body posture selectivity of macaque inferotemporal neurons. Society for Neuroscience .
Keypoint-based modeling of body posture selectivity of macaque inferotemporal neurons
Abstract:

Non-verbal social communication relies on the interpretation of visual cues from the body. fMRI studies in macaques have identified regions within the inferotemporal (IT) cortex that exhibit heightened activation to bodies compared to faces and objects. Among these regions, the ventral bank Superior Temporal Sulcus (STS) patches, i.e. the mid STS (MSB) and anterior STS body patch (ASB), show selectivity for static (and dynamic) bodies. However, the body features that drive the response of these neurons, in particular their representation of body posture, within these two levels of processing are unclear. To investigate this, we recorded multi- unit responses, using 16-channel V-probes, within and around MSB and ASB in two monkeys, employing a stimulus set comprising 720 stimuli featuring a monkey avatar in 45 body postures, rendered from 16 viewing angles. The static stimuli were presented during passive fixation. We employed principal component regression to model the response of the neurons based on the 10 principal components of 22 2D body keypoints extracted from the stimuli, which explained about 90% of the stimulus variance. Of the body-category selective neurons (at least twofold higher response to dynamic bodies compared to dynamic faces and objects), the 2D key-point-based model explained the selectivity for body posture and view with a median reliability-corrected coefficient of determination of 0.42 and 0.20 in the MSB and ASB regions, respectively. Inclusion of the depth dimension increased the model fit significantly for ASB but not MSB. When comparing with a convolutional neural network (CNN; ResNet50-robust; regression on 50 PCs) feature-based approach, the keypoint-based model exhibited slightly inferior performance, particularly in ASB, when focusing on higher-layer features but remained superior to the lower- layer features-based CNN model. Inverting the keypoint models allowed visualization of the body features that drove the posture selectivity of the neurons. We found that these body features ranged from local body features like the upper limbs or tail to combinations of them, but rarely the entire body. Some neurons, even in the mid STS region, tolerated changes in the view of the preferred body parts. The view tolerance was significantly greater in ASB compared to MSB. Our study shows that a body keypoint representation explains a sizable proportion of the selectivity to body posture and view of macaque visual cortical neurons, especially in the mid STS. Furthermore, the modeling suggests that 3D cues contribute to the body selectivity of anterior but not posterior IT neurons.

Authors: Vogels, Rufin Raman, R. Nejad, G. G. Mukovskiy, Albert; Lappe, Alexander; Giese, Martin A.; Martini, Lucas M.; Bognár, A.
Research Areas: Uncategorized
Type of Publication: In Collection
[Bibtex]
Marrazzo, G., Martino, F. D., Mukovskiy, A., Giese, M. A. & de Gelder, B (2024). Voxelwise encoding of biomechanics in occipitotemporal cortex using dynamic body stimuli at ultra-high field 7T. Society for Neuroscience .
Voxelwise encoding of biomechanics in occipitotemporal cortex using dynamic body stimuli at ultra-high field 7T
Abstract:

In this fMRI study we investigated the role played by biomechanical plausibility in the representation of bodies in EBA. The extrastriate body area (EBA) (Downing et al. 2001, Peelen and Downing, 2005) is currently considered to be a ventral cortex object category area, selective for body stimuli but little yet is understood about its computational functions. In a previous study we showed that the EBA is sensitive to joints position in still body stimuli (Marrazzo et al. 2023). Here, we used video images to investigate whether disrupting joints configuration affects the representation of bodies in EBA. Stimuli depicted artificial whole-body movements and were generated from the MoVI dataset. We selected 60 trials of naturalistic body movement and created 60 (possible) videos. Additionally, these stimuli underwent further processing where elbows and knees position/angle were manually modified, to create (from possible stimuli) biomechanically impossible stimuli. Therefore, the stimuli set included 120 videos (60 possible, 60 impossible). 12 participants were scanned using a 7T (T2*-weighted Multi-Band accelerated EPI 2D BOLD sequence, MB = 2, voxel size = 0.8 mm3, TR = 2300 ms, TE = 27 ms) in a fast event-related design over 12 separate runs. Each run consisted of 20 unique stimuli (10 possible, 10 impossible repeated 6 times across the 12 runs) which appeared on the screen for 2-3 s. Participants were asked to fixate and attention was controlled using catch trials (fixation shape change). The fMRI response was modeled using several features extracted from the stimuli: 3D coordinates and rotation matrices of key joints (kp/rot) and a model which represents within\between distance between joints for each video as a mean to encode biomechanical information (simdist). The fMRI predicted responses from each model were generated via banded ridge regression (Nunez-Elizalde et al. 2019, Dupré La Tour et al. 2022) using crossvalidation. Results show a pattern of responses across visual cortex with simdist and kp model best predicting responses to our stimuli. Specifically, the simdist representation shows higher prediction accuracy in in high-level temporal areas such as EBA outperforming the kp model. These findings expand on previous research showing that EBA codes for specific features of the body, which in the case of kp model, are the joints position (Marrazzo et al. 2023). Additionally, EBA shows high degree of sensitivity for joints configuration to the point that biomechanically possible/impossible bodies appear to be differentially encoded. Acknowledgments: This work was supported by ERC 2019-SyG-RELEVANCE-856495

Authors: Marrazzo, G. Martino, F. De Mukovskiy, Albert; Giese, Martin A.; de Gelder, Beatrice
Research Areas: Uncategorized
Type of Publication: In Collection
[Bibtex]
Lappe, A., Bognár, A., Nejad, G. G., Mukovskiy, A., Giese, M. A. & Vogels, R (2024). Encoding of bodies and objects in body-selective neurons. 2024 Neuroscience Meeting Planner .
Encoding of bodies and objects in body-selective neurons
Authors: Lappe, Alexander; Bognár, Anna Nejad, Ghazal Ghamkhari Mukovskiy, Albert; Giese, Martin A.; Vogels, Rufin
Research Areas: Uncategorized
Type of Publication: In Collection
[Bibtex]
Bognár, A., Mukovskiy, A., Nejad, G. G., Taubert, N., Stettler, M., Martini, L. M. et al (2023). Simultaneous recordings from posterior and anterior body responsive regions in the macaque Superior Temporal Sulcus . VSS 2023, May 19-24 2023, St. Pete Beach, Florida.
Simultaneous recordings from posterior and anterior body responsive regions in the macaque Superior Temporal Sulcus
Authors: Bognár, A. Mukovskiy, Albert; Nejad, G. Ghamkhari Taubert, Nick; Stettler, Michael; Martini, Lucas M.; Raman, R. Giese, Martin A.; Vogels, R.
Type of Publication: In Collection
[Bibtex]
Bognár, A., Mukovskiy, A., Nejad, G. G., Taubert, N., Stettler, M., Martini, L. M. et al (2023). Feature selectivity of body-patch neurons assessed with a large set of monkey avatars . 13th Annual Meeting on PrimateNeurobiology, Apr.26-28 2023, Göttingen Primate Center..
Feature selectivity of body-patch neurons assessed with a large set of monkey avatars
Authors: Bognár, A. Mukovskiy, Albert; Nejad, G. Ghamkhari Taubert, Nick; Stettler, Michael; Martini, Lucas M.; Raman, R. Giese, Martin A.; Vogels, R.
Type of Publication: In Collection
[Bibtex]
Mukovskiy, A., Hovaidi-Ardestani, M., Salatiello, A., Stettler, M., Vogels, R. & Giese, M. A (2022). Neurophysiologically-inspired computational model of the visual recognition of social behavior and intent . FENS Forum, Paris.
Neurophysiologically-inspired computational model of the visual recognition of social behavior and intent
Abstract:

AIMS: Humans recognize social interactions and intentions from videos of moving abstract stimuli, including simple geometric figures (Heider {&} Simmel, 1944). The neural machinery supporting such social interaction perception is completely unclear. Here, we present a physiologically plausible neural model of social interaction recognition that identifies social interactions in videos of simple geometric figures and fully articulating animal avatars, moving in naturalistic environments. METHODS: We generated the trajectories for both geometric and animal avatars using an algorithm based on a dynamical model of human navigation (Hovaidi-Ardestani, et al., 2018, Warren, 2006). Our neural recognition model combines a Deep Neural Network, realizing a shape-recognition pathway (VGG16), with a top-level neural network that integrates RBFs, motion energy detectors, and dynamic neural fields. The model implements robust tracking of interacting agents based on interaction-specific visual features (relative position, speed, acceleration, and orientation). RESULTS: A simple neural classifier, trained to predict social interaction categories from the features extracted by our neural recognition model, makes predictions that resemble those observed in previous psychophysical experiments on social interaction recognition from abstract (Salatiello, et al. 2021) and naturalistic videos. CONCLUSION: The model demonstrates that recognition of social interactions can be achieved by simple physiologically plausible neural mechanisms and makes testable predictions about single-cell and population activity patterns in relevant brain areas. Acknowledgments: ERC 2019-SyG-RELEVANCE-856495, HFSP RGP0036/2016, BMBF FKZ 01GQ1704, SSTeP-KiZ BMG: ZMWI1-2520DAT700, and NVIDIA Corporation.

Authors: Mukovskiy, Albert; Hovaidi-Ardestani, Mohammad Salatiello, Alessandro; Stettler, Michael; Vogels, Rufin Giese, Martin A.
Type of Publication: In Collection
[Bibtex]
Mukovskiy, A., Hovaidi-Ardestani, M., Salatiello, A., Stettler, M., Vogels, R. & Giese, M. A (2022). Physiologically-inspired neural model for social interaction recognition from abstract and naturalistic videos . VSS Annual Meeting 2022.
Physiologically-inspired neural model for social interaction recognition from abstract and naturalistic videos
Authors: Mukovskiy, Albert; Hovaidi-Ardestani, Mohammad Salatiello, Alessandro; Stettler, Michael; Vogels, Rufin Giese, Martin A.
Type of Publication: In Collection
[Bibtex]
Giese, M. A., Mukovskiy, A., Hovaidi-Ardestani, M., Salatiello, A. & Stettler, M. (2021). Neurophysiologically-inspired model for social interactions recognition from abstract and naturalistic stimuli. VSS 2021.
Neurophysiologically-inspired model for social interactions recognition from abstract and naturalistic stimuli
Research Areas: Uncategorized
Type of Publication: Article
[Bibtex]
Giese, M. A., Mukovskiy, A., Hovaidi-Ardestani, M., Salatiello, A. & Stettler, M (2021). Neurophysiologically-inspired model for social interactions recognition from abstract and naturalistic stimuli. VSS 2021, May 21-26 .
Neurophysiologically-inspired model for social interactions recognition from abstract and naturalistic stimuli
Type of Publication: In Collection
[Bibtex]
Mukovskiy, A., Ardestani, M. H., Salatiello, A., Stettler, M. & Giese, M. A (2021). Physiologically-inspired neural model for social interactions recognition from abstract and naturalistic stimuli. Göttingen Meeting of the German Neuroscience Society 2021, Germany .
Physiologically-inspired neural model for social interactions recognition from abstract and naturalistic stimuli
Type of Publication: In Collection
[Bibtex]
Mukovskiy, A., Ardestani, M. H., Salatiello, A., Stettler, M. & Giese, M. A. (2021). Physiologically-inspired neural model for social interactions recognition from abstract and naturalistic stimuli.. Göttingen Meeting of the German Neuroscience Society.
Physiologically-inspired neural model for social interactions recognition from abstract and naturalistic stimuli.
Research Areas: Uncategorized
Type of Publication: Article
[Bibtex]
Ardestani, M. H., Mukovskiy, A., Stettler, M., Saini, N. & Giese, M. A (2020). Physiologically-inspired neural model for the visual recognition of social interactions from abstract and natural stimuli. VSS 2020, 19-24 Jun .
Physiologically-inspired neural model for the visual recognition of social interactions from abstract and natural stimuli
Authors: Ardestani, Mohammad Hovaidi Mukovskiy, Albert; Stettler, Michael; Saini, Nitin Giese, Martin A.
Type of Publication: In Collection
[Bibtex]
Kodl, J., Mukovskiy, A., Mohammadi, P., Malekzadeh, M., Taubert, N., Christensen, A. et al (2019). Online planning and control of ball throwing by the humanoid robot COMAN and validation exploiting VR in rehabilitation scenarios with ataxia patients. Oral presentation and extended abstract in Proc. of CYBATHLON Symposium on Assistive and Wearable Robotics (AsWeR 2019). 16–17 May, 2019, Karlsruhe .
Online planning and control of ball throwing by the humanoid robot COMAN and validation exploiting VR in rehabilitation scenarios with ataxia patients
Authors: Kodl, Jindrich Mukovskiy, Albert; Mohammadi, P Malekzadeh, M Taubert, Nick; Christensen, A Dijkstra, Tjeerd Steil, JJ Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
Full text: Online version
[Bibtex]
Mohammadi, P., Malekzadeh, M. S., Kodl, J., Mukovskiy, A., Wigand, D., Giese, M. A. et al. (2018). Real-Time Control of Whole-Body Robot Motion and Trajectory Generation for Physiotherapeutic Juggling in VR. IROS2018, 270-277.
Real-Time Control of Whole-Body Robot Motion and Trajectory Generation for Physiotherapeutic Juggling in VR
Authors: Mohammadi, P. Malekzadeh, M. S. Kodl, Jindrich Mukovskiy, Albert; Wigand, D. Giese, Martin A.; Steil, Jochen
Type of Publication: Article
Full text: PDF Online version
[Bibtex]
Mukovskiy, A. (2018). Computational Methods for Cognitive and Cooperative Robotics. Phd Thesis.
Computational Methods for Cognitive and Cooperative Robotics
Research Areas: Uncategorized
Type of Publication: Phd Thesis
Month: September
Full text: Online version
[Bibtex]
Kodl, J., Mukovskiy, A., Dijkstra, T., Brötz, D., Ludolph, N., Taubert, N. et al. (2017). Ball Throwing Games in Virtual Reality for Motor Rehabilitation. Iberdiscap, Bogota, Colombia, ISSN 2619-6433, 489-496.
Ball Throwing Games in Virtual Reality for Motor Rehabilitation
Authors: Kodl, Jindrich Mukovskiy, Albert; Dijkstra, Tjeerd Brötz, D Ludolph, Nicolas Taubert, Nick; Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF Online version
[Bibtex]
Kodl, J., Mukovskiy, A., Dijkstra, T., Brötz, D., Ludolph, N., Taubert, N. et al (2017). Ball Throwing Games in Virtual Reality for Motor Rehabilitation. IX Iberoamerican Congress in Assistive Technology, Iberdiscap, Bogota, Colombia, ISSN 2619-6433 .
Ball Throwing Games in Virtual Reality for Motor Rehabilitation
Authors: Kodl, Jindrich Mukovskiy, Albert; Dijkstra, Tjeerd Brötz, D Ludolph, Nicolas Taubert, Nick; Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
[Bibtex]
Mukovskiy, A., Vassallo, C., Naveau, M., Stasse, O., Souères, P. & Giese, M. A. (2017). Adaptive synthesis of dynamically feasible full-body movements for the humanoid robot HRP-2 by flexible combination of learned dynamic movement primitives. Robotics and Autonomous Systems, 91, 270.
Adaptive synthesis of dynamically feasible full-body movements for the humanoid robot HRP-2 by flexible combination of learned dynamic movement primitives
Authors: Mukovskiy, Albert; Vassallo, Christian Naveau, Maximilien Stasse, Olivier Souères, Philippe Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF Online version
[Bibtex]
Mukovskiy, A., Taubert, N., Endres, D., Vassallo, C., Naveau, M., Stasse, O. et al. (2017). Modeling of coordinated human body motion by learning of structured dynamic representations. In: J.P. Laumond et al. (Eds.): "Geometric and Numerical Foundations of Movements," Springer STAR Series, Springer-Verlag Berlin Heidelberg., 117, 237-267.
Modeling of coordinated human body motion by learning of structured dynamic representations
Authors: Mukovskiy, Albert; Taubert, Nick; Endres, Dominik Vassallo, C Naveau, M Stasse, O Souères, P Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF Online version
[Bibtex]
Sternad, D., Mukovskiy, A., Ebert, J. & Dijkstra, T (2016). Dynamic Stability in Human Control of Complex Object. Ann. Meeting of NCM Soc., April, 2016, Montego Bay .
Dynamic Stability in Human Control of Complex Object
Authors: Sternad, D Mukovskiy, Albert; Ebert, J Dijkstra, Tjeerd
Research Areas: Uncategorized
Type of Publication: In Collection
Full text: PDF
[Bibtex]
Karklinsky, M., Naveau, M., Mukovskiy, A., Stasse, O., Flash, T. & Souères, P. (2016). Robust human-inspired power law trajectories for humanoid HRP-2 robot. Biomedical Robotics and Biomechatronics, 6th IEEE Conference Paper, 106-113.
Robust human-inspired power law trajectories for humanoid HRP-2 robot
Authors: Karklinsky, M Naveau, M Mukovskiy, Albert; Stasse, O Flash, T Souères, P
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF Online version
[Bibtex]
Mukovskiy, A., Vassallo, C., Naveau, M., Stasse, O., Souères, P. & Giese, M. A (2015). Learning Movement Primitives for the Humanoid Robot HRP2. IROS 2015: Joint workshop by the EU FP7 projects KoroiBot and H2R, Hamburg, Germany .
Learning Movement Primitives for the Humanoid Robot HRP2
Authors: Mukovskiy, Albert; Vassallo, Christian Naveau, Maximilien Stasse, Olivier Souères, Philippe Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
[Bibtex]
Ebert, J., Mukovskiy, A., Dijkstra, T. & Sternad, D (2015). Why You Don't Spill Your Coffee. RISE 2015: Research, Innovation and Scholarship Expo, Northeastern University, Boston, MA .
Why You Don't Spill Your Coffee
Authors: Ebert, Julia Mukovskiy, Albert; Dijkstra, Tjeerd Sternad, Dagmar
Research Areas: Uncategorized
Type of Publication: In Collection
Full text: PDF
[Bibtex]
Mukovskiy, A., Land, W. M., Schack, T. & Giese, M. A. (2015). Modeling of predictive human movement coordination patterns for applications in computer graphics. Journal of WSCG, 23(2), 139-146.
Modeling of predictive human movement coordination patterns for applications in computer graphics
Authors: Mukovskiy, Albert; Land, William M. Schack, Tomas Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF Online version
[Bibtex]
Dayan, E., Stella, I., Mukovskiy, A., Douek, Y., Giese, M. A., Malach, R. et al. (2014). The Default Mode Network Differentiates Biological From Non-Biological Motion. Cereb. Cortex,, 26(1), 234-245.
The Default Mode Network Differentiates Biological From Non-Biological Motion
Authors: Dayan, Eran Stella, I. Mukovskiy, Albert; Douek, Yehonatan Giese, Martin A.; Malach, Rafael Flash, Tamar
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF Online version
[Bibtex]
Chiovetto, E., Mukovskiy, A., Reinhart, F., Kansari-Zadeh, M. S., Billiard, A., Steil, J. et al (2014). Assessment of human-likeness and naturalness of interceptive arm reaching movement accomplished by a humanoid robot Perception 43 ECVP Abstract Supplement, page 107.
Assessment of human-likeness and naturalness of interceptive arm reaching movement accomplished by a humanoid robot
Authors: Chiovetto, Enrico Mukovskiy, Albert; Reinhart, F. Kansari-Zadeh, M. S. Billiard, Aude Steil, Jochen Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
Full text: PDF Online version
[Bibtex]
Mukovskiy, A. & Giese, M. A (2014). Kinematic planning and dynamic control for bipeds. The XX Congress of the Int. Soc. of Electrophysiology and Kinesiology, ISEK 2014, Rome, Italy .
Kinematic planning and dynamic control for bipeds
Research Areas: Uncategorized
Type of Publication: In Collection
Full text: PDF
[Bibtex]
Ajallooeian, M., van den Kieboom, J., Mukovskiy, A., Giese, M. A. & Ijspeert, A. J. (2013). A general family of morphed nonlinear phase oscillators with arbitrary limit cycle shape. Physica D: Nonlinear Phenomena, 263, 41-56.
A general family of morphed nonlinear phase oscillators with arbitrary limit cycle shape
Authors: Ajallooeian, Mostafa van den Kieboom, J. Mukovskiy, Albert; Giese, Martin A.; Ijspeert, Auke J.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF
[Bibtex]
Mukovskiy, A., Slotine, J.-J. & Giese, M. A. (2013). Dynamically stable control of articulated crowds. Journal of Computational Science, 4(4), 304-310.
Dynamically stable control of articulated crowds
Authors: Mukovskiy, Albert; Slotine, Jean-Jacques E. Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF
[Bibtex]
Mukovskiy, A., Slotine, J. J. & Giese, M. A. (2011). Analysis and design of the dynamical stability of collective behavior in crowds. Skala V. (ed): Proceedings of the 19th International Conference on Computer Graphics, Visualization and Computer Vision 2011(WSCG2011), Jan.31-Febr.3, 2011, Plzen, Czech Republic. Journal of WSCG, 19(1-3), 69-76.
Analysis and design of the dynamical stability of collective behavior in crowds
Authors: Mukovskiy, Albert; Slotine, Jean Jacques E. Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF
[Bibtex]
Mukovskiy, A., Slotine, J.-J. & Giese, M. A. (2010). Analysis of the global dynamical stability of crowd navigation applying Contraction Theory. In: Electronic Proceedings of Workshop on Crowd Simulation, 23rd Int. Conference on Computer Animation and Social Agents (CASA 2010), May 31-June 2, 2010, Saint-Malo, France.
Analysis of the global dynamical stability of crowd navigation applying Contraction Theory
Authors: Mukovskiy, Albert; Slotine, Jean-Jacques E. Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF
[Bibtex]
Baranauskas, G., Mukovskiy, A., Wolf, J. & Volgushev, M. (2010). The determinants of the onset dynamics of action potentials in a computational model. Neuroscience, 167(4), 1070-1090.
The determinants of the onset dynamics of action potentials in a computational model
Authors: Baranauskas, Gytis Mukovskiy, Albert; Wolf, Julia Volgushev, Maxim
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF
[Bibtex]
Mukovskiy, A., Slotine, J.-J. & Giese, M. A. (2010). Contraction theory as method for the analysis and the design of stability of collective behavior in crowds. In: Electronic Proc. IADIS Int. Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2010) 27-29 July,2010 Freiburg, Germany, 47-56.
Contraction theory as method for the analysis and the design of stability of collective behavior in crowds
Authors: Mukovskiy, Albert; Slotine, Jean-Jacques E. Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF
[Bibtex]
Mukovskiy, A., Slotine, J.-J. & Giese, M. A. (2010). Design of the Dynamic Stability Properties of the Collective Behavior of Articulated Bipeds. Proceedings of 10th IEEE-RAS Int. Conference on Humanoid Robots,( Humanoids 2010) December 6-8, 2010, Nashville, TN, USA. pp. 66-73. In press in special issue of IEEE Journal on Robotics and Automation.
Design of the Dynamic Stability Properties of the Collective Behavior of Articulated Bipeds
Authors: Mukovskiy, Albert; Slotine, Jean-Jacques E. Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: PDF
[Bibtex]
Giese, M. A., Mukovskiy, A., Park, A.-N., Omlor, L. & Slotine, J.-J. (2009). Real-Time Synthesis of Body Movements Based on Learned Primitives. In Cremers D, Rosenhahn B, Yuille A L (eds): Statistical and Geometrical Approaches to Visual Motion Analysis, Lecture Notes in Computer Science, 5604, 107-127.
Real-Time Synthesis of Body Movements Based on Learned Primitives
Authors: Giese, Martin A.; Mukovskiy, Albert; Park, Aee-Ni Omlor, Lars Slotine, Jean-Jacques E.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
[Bibtex]
Park, A.-N., Mukovskiy, A., Slotine, J.-J. & Giese, M. A. (2009). Design of dynamical stability properties in character animation. In: The 6th Workshop on Virtual Reality Interaction and Physical Simulation. ,VRIPHYS 09, Nov 5-6, Karlsruhe,Germany, 85-94.
Design of dynamical stability properties in character animation
Authors: Park, Aee-Ni Mukovskiy, Albert; Slotine, Jean-Jacques E. Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: Article
Full text: Online version
[Bibtex]
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
[Bibtex]
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
[Bibtex]
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
[Bibtex]
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
[Bibtex]
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