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{ "pk": 27629, "title": "Learning Temporal Generative Neural Codes for Biological Motion Perceptionand Inference", "subtitle": null, "abstract": "We introduce a modular recurrent neural architecture, which learns distributed, generative temporal models of bio-logical motion. It encodes modal visual and proprioceptive (angular) biological motions separately by means of autoencoders,structuring respective postures, motion directions, and motion magnitudes separately. The submodal encoders are interdepen-dent by predicting each other’s next autoencoder states temporally. As a result, distributed attractor states can develop fromself-generated motions. We show that the architecture is able to synchronize its activities across modalities towards overallconsistent action-encoding attractors. Moreover, the developing spatial and temporal structures can complete partially observ-able actions, e.g., when only providing visual information. Furthermore, we show that the network is capable of simulatingwhole-body actions without any sensory stimulation, thus imagining unfolding actions. Finally, we show that the network isable to infer the visual perspective on a biological motion. Thus, the neural architecture enables embodied perspective takingand action inference.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [], "section": "Posters: Member Abstracts", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/6c76q60r", "frozenauthors": [ { "first_name": "Fabian", "middle_name": "", "last_name": "Schrodt", "name_suffix": "", "institution": "University of T ̈ubingen", "department": "" }, { "first_name": "Martin", "middle_name": "V.", "last_name": "Butz", "name_suffix": "", "institution": "University of T ̈ubingen", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2017-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/27629/galley/17265/download/" } ] }