A number of neural network implementations of control theory algorithms have been proposed, especially with the recent rise of machine learning. However, most current implementations are biologically unrealistic either in their mechanisms or in their learning algorithms. Considering mechanism, spiking neural network implementations of control algorithms have the potential of being deployed in neuromorphic hardware and neuro-robotics for energy-efficient portable robotics, as well as suggesting neural circuits for biological movement control. Considering the issue of learning, backpropagation of error (derived from gradient descent) is the key technique for deep learning in artificial neural networks. Even though artificial neural networks are inspired by the brain, it is unclear if learning in the brain utilises (an approximation of) backpropagation of error. Learning in the brain happens via synaptic plasticity, which being a physical process and not an algorithm, has to depend on quantities available physically near the synapse at the current time, i.e. the learning rule has to be local in space and time. However, standard backpropagation does not fulfill this criterion.
In this PhD project, we will distill the benefits of various control theory approaches into a spiking neural network model, that is biologically plausible in mechanism, architecture and learning, and possibly outperforms similar current approaches. We will benchmark these control networks on biological limb and prosthetic models. We will compare these networks with experimental data and port them to neuromorphic hardware as well.
Currently, only students with their own funding can be considered.
A background in Control theory, Physics and/or Computational neuroscience, with good coding skills, is desirable.
If English is not your first language, you must have an IELTS score of 6.5 overall, with no less than 6.0 in each component. More details regarding English Language Qualifications can be found here: Language Requirements
To apply for the project, applicants need to apply directly to the University of Sheffield using the online application system. Complete an application for admission to the standard Computer Science PhD programme here: Apply Now.
Please name Dr Aditya Gilra as your proposed supervisor in your application.
Applications should include a research proposal, transcripts and two references.
The research proposal (up to 4 A4 pages, including references) should outline your reasons for applying for this project and how you would approach the researching, including details of your skills and experience.