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Generative adversarial networks as integrated forward and inverse model for motor control
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Generativa konkurrerande nätverk som integrerad framåtriktad och invers modell för rörelsekontroll (Swedish)
Abstract [en]

Internal models are believed to be crucial components in human motor control. It has been suggested that the central nervous system (CNS) uses forward and inverse models as internal representations of the motor systems. However, it is still unclear how the CNS implements the high-dimensional control of our movements. In this project, generative adversarial networks (GAN) are studied as a generative model of movement data. It is shown that, for a relatively small number of effectors, it is possible to train a GAN which produces new movement samples that are plausible given a simulator environment. It is believed that these models can be extended to generate high-dimensional movement data. Furthermore, this project investigates the possibility to use a trained GAN as an integrated forward and inverse model for motor control.

Abstract [sv]

Interna modeller tros vara en viktig del av mänsklig rörelsekontroll. Det har föreslagits att det centrala nervsystemet (CNS) använder sig av framåtriktade modeller och inversa modeller för intern representation av motorsystemen. Dock är det fortfarande okänt hur det centrala nervsystemet implementerar denna högdimensionella kontroll. Detta examensarbete undersöker användningen av generativa konkurrerande nätverk som generativ modell av rörelsedata. Experiment visar att dessa nätverk kan tränas till att generera ny rörelsedata av en tvådelad arm och att den genererade datan efterliknar träningsdatan. Vi tror att nätverken även kan modellera mer högdimensionell rörelsedata. I projektet undersöks även användningen av dessa nätverk som en integrerad framåtriktad och invers modell.

Place, publisher, year, edition, pages
2017. , p. 61
Keywords [en]
Generative adversarial networks, Internal models, Motor Control
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-220535OAI: oai:DiVA.org:kth-220535DiVA, id: diva2:1169168
External cooperation
Bernstein Center Freiburg, University of Freiburg
Educational program
Master of Science in Engineering -Engineering Physics
Presentation
(English)
Supervisors
Examiners
Available from: 2017-12-27 Created: 2017-12-22 Last updated: 2018-01-13Bibliographically approved

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