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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.

Research Areas: Neural and Computational Principles of Action and Social Processing

Type of Publication: In Collection

JRESEARCH_BOOK_TITLE: Artificial Neural Networks and Machine Learning -- ICANN 2023

Publisher: Springer Nature Switzerland

Editor: Iliadis, Lazaros and Papaleonidas, Antonios and Angelov, Plamen and Jayne, Chrisina

Address: Cham

Pages: 533-544

ISBN: 978-3-031-44210-0