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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

Projects

Publications

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
Lappe, A., Bognár, A., Nejad, G. G., Mukovskiy, A., Martini, L. M., Giese, M. A. et al. (2024). Parallel Backpropagation for Shared-Feature Visualization..
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: Misc
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
Journal: Journal of Neuroscience
Volume: 44
Number: 40
Year: 2024
ISSN: 0270-6474
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
Type of Publication: In Collection
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
Type of Publication: In Collection
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