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M. Sc. Kumar, Prerana

5.522a
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
+4970712989137
prerana.kumar@uni-tuebingen.de
Prerana Kumar

Projects

Publications

Kumar, P., Raman, R., Bognár, A., Taubert, N., Nejad, G. G., Vogels, R. et al (2024). Neural models for the visual recognition of static body poses and dynamic body movements. 2024 Neuroscience Meeting Planner .
Neural models for the visual recognition of static body poses and dynamic body movements
Authors: Kumar, Prerana; Raman, Rajani Bognár, Anna Taubert, Nick; Nejad, Ghazal Ghamkhari Vogels, Rufin Giese, Martin A.
Research Areas: Uncategorized
Type of Publication: In Collection
JRESEARCH_BOOK_TITLE: 2024 Neuroscience Meeting Planner
Organization: Society for Neuroscience
[Bibtex]
Kumar, P., Taubert, N., Raman, R., Bognár, A., Nejad, G. G., Vogels, R. et al. (2023). Neurodynamical Model of the Visual Recognition of Dynamic Bodily Actions from Silhouettes. , 533-544.
Neurodynamical Model of the Visual Recognition of Dynamic Bodily Actions from Silhouettes
Abstract:

For social species, including primates, the recognition of dynamic body actions is crucial for survival. However, the detailed neural circuitry underlying this process is currently not well understood. In monkeys, body-selective patches in the visual temporal cortex may contribute to this processing. We propose a physiologically-inspired neural model of the visual recognition of body movements, which combines an existing image-computable model (`ShapeComp') that produces high-dimensional shape vectors of object silhouettes, with a neurodynamical model that encodes dynamic image sequences exploiting sequence-selective neural fields. The model successfully classifies videos of body silhouettes performing different actions. At the population level, the model reproduces characteristics of macaque single-unit responses from the rostral dorsal bank of the Superior Temporal Sulcus (Anterior Medial Upper Body (AMUB) patch). In the presence of time gaps in the stimulus videos, the predictions made by the model match the data from real neurons. The underlying neurodynamics can be analyzed by exploiting the framework of neural field dynamics.

Authors: Kumar, Prerana; Taubert, Nick; Raman, Rajani Bognár, Anna Nejad, Ghazaleh Ghamkhari Vogels, Rufin Giese, Martin A.
Type of Publication: Article
Pages: 533-544
Year: 2023
[Bibtex]
Kumar, P., Taubert, N., Raman, R., Vogels, R., de Gelder, B. & Giese, M. A (2022). Neural model for the representation of static and dynamic bodies in cortical body patches . VSS 2022.
Neural model for the representation of static and dynamic bodies in cortical body patches
Authors: Kumar, Prerana; Taubert, Nick; Raman, Rajani Vogels, Rufin de Gelder, Beatrice Giese, Martin A.
Type of Publication: In Collection
[Bibtex]
Kumar, P., Taubert, N., Raman, R., Vogels, R., de Gelder, B. & Giese, M. A (2021). Physiologically-inspired neural model for the visual recognition of dynamic bodies . Neuroscience 2021.
Physiologically-inspired neural model for the visual recognition of dynamic bodies
Authors: Kumar, Prerana; Taubert, Nick; Raman, Rajani Vogels, Rufin de Gelder, Beatrice Giese, Martin A.
Type of Publication: In Collection
[Bibtex]
Kumar, P., Taubert, N., Stettler, M., Vogels, R., de Gelder, B. & Giese, M. A (2021). Neurodynamical model for the visual recognition of dynamic bodies . ECVP 2021.
Neurodynamical model for the visual recognition of dynamic bodies
Authors: Kumar, Prerana; Taubert, Nick; Stettler, Michael; Vogels, Rufin de Gelder, Beatrice Giese, Martin A.
Type of Publication: In Collection
[Bibtex]
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