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.
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