Qualification Type: | PhD |
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Location: | Birmingham |
Funding for: | UK Students, EU Students, International Students |
Funding amount: | This studentship is funded by the MIBTP. |
Hours: | Full Time |
Placed On: | 22nd November 2024 |
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Closes: | 16th January 2025 |
The objective of this project is to analyse the latest experimental data on neuronal coding of motor behaviours in humans, with the goal of developing a functional model based on these findings. The model will aim to be both biologically descriptive and computationally implementable, focusing on complex finger movements involving various sequential patterns, extending beyond simple motor protocols.
Motor planning, the cognitive process that precedes voluntary movement, is essential for executing skilled actions. Uncovering the neural mechanisms behind motor planning is key to understanding how the brain coordinates complex, sequential behaviours. This research will investigate the neural dynamics involved in motor planning, with an emphasis on how the brain organizes and prepares motor sequences. Previous studies suggest that neural competitive queuing of ordinal structures plays a central role in skilled sequential actions, allowing the brain to resolve competition between movements. Furthermore, recent research highlights the use of expansive null-space representations in motor cortex of non-human primate, which prepare movements while preventing premature execution.
Building on these insights, this project will explore how neural networks within the brain coordinate motor planning, focusing on exploring neural dynamics in different parts of brain areas.
Human recordings conducted at the CHBH in Birmingham provide a rich foundation of experimental data for this study. The data, gathered through fMRI, EEG, and MEG modalities, will inform the development of a theoretical model that is consistent with both these new datasets and previously published results. The project will also involve designing new experiments based on the model’s theoretical predictions to validate or refine the proposed framework.
By integrating advanced artificial neural networks and AI techniques, we aim to build a comprehensive model of motor planning that aligns with experimental findings. This model will provide valuable insights into human motor planning, offering a deeper understanding beyond current non-human primate studies.
The project will be supervised by Dr Jian Liu (j.liu.22@bham.ac.uk).
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