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Department of Computer Science

Biologically-Plausible Continual Learning

Primary supervisor

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Other projects with the same supervisor


  • Competition Funded Project (Students Worldwide)

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

Continual learning (aka lifelong learning) refers to a problem on how a learning system learns multiple tasks in succession over the lifespan where later tasks do not degrade the performance of the system learned for the earlier tasks and, ideally, the system can leverage the knowledge learned in previous tasks to facilitate learning the new tasks better. While human brains have such a remarkable capability to learn various tasks without negatively interference during lifelong learning, all machine learning models, deep learning models in particular, generally fail for continual learning due to the notorious "catastrophic forgetting" phenomenon. Recently, efforts have been made in addressing this issue in deep learning research but all the attempts so far are for artificial neural networks without taking biological plausibility into account. On the other hand, to a great degree, continual learning mechanisms in human brains remain unknown.

The project is going to investigate and develop biologically-plausible continual learning mechanisms based on biologically-plausible neural networks, e.g., spiking neural networks, and the existing evidence from neuroscience and cognitive science via carefully formulated hypotheses. In this project, main issues to be studied include formulating proper hypotheses, developing biologically-plausible building blocks and learning algorithms required by continual learning, experimentation to verify the formulated hypotheses and exploring the possibility of using the research outcome to inform other disciplines. Also, this project includes running real-time simulations on a neuromorphic computer, e.g., SpiNNaker, subject to its availability. In this case, how to map biologically-plausible continual learning mechanisms properly onto the neuromorphic computer is going to be studied as well. In general, this project is suitable for one who is interested in fundamental research in biologically-plausible deep learning and exploring the unknown aspects of human brains.

It is worth highlighting that this is an extremely challenging project of a great novelty. In order to take this project, it is essential to be self-motivated and to have decent background knowledge in mathematics and machine learning as well as good programming skills. It would be ideal if one has the research experience in spiking neural networks and computational cognitive modelling.

If you are interested in this project, please first visit my research student page: for the required materials and information prior to contacting me.

Person specification

For information


Applicants will be required to evidence the following skills and qualifications.

  • This project requires mathematical engagement and ability substantially greater than for a typical Computer Science PhD. Give evidence for appropriate competence, as relevant to the project description.
  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.


Applicants will be required to evidence the following skills and qualifications.

  • You will have good time management.
  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.


Applicants will be required to address the following.

  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Why do you believe you are suitable for doing Postgraduate Research?