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Memory based machine intelligence technique development

The purpose of this research project is to model the human brain and develop a memory mechanism based on next-generation Artificial Intelligence (AI) algorithms. The human brain can be modeled using different ways such as pure mathematical models, deep graphic neural networks, or spiking neural networks. Some sensors such as EEG, near Infrared imaging device, will be used to monitor the brain activities during vision tasks.

The new AI algorithms will be developed to implement the memory functions such as storing, recalling, information abstracting and supporting reasoning. Image data and vision tasks will be used for the evaluation of these algorithms for unlimited storing, fast image retrieval and recognition. The algorithms should not only provide high accuracy but also works with low computing cost or power consumption.

For the implementation, different hardware such as CPU, GPU, FPGA and neuromorphic chips can be explored for fast implementation.

This project is a further development from the previous project: Neuromorphic computing - simulating the human brain memory mechanism.

The successful candidate should hold an Honours Degree or a Master’s Degree in Mathematics, Data Science, Computer Science, Electronic Engineering, or another relevant subject area. Candidates with knowledge or research experience in sensors, signal processing, computer vision, machine learning, and strong programming skills are preferred.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)


Hongying Meng - Dr Hongying Meng is a Reader with Department of Electronic and Electrical Engineering, College of Engineering, Design and Physical Sciences, Brunel University London. Before that, he held research positions in several UK universities including University College London (UCL), University of YorkUniversity of Southampton, University of Lincoln, and University of Dundee. He received his Ph.D. degree in Communication and Electronic Systems from Xi’an Jiaotong University and was a lecturer in Electronic Engineering Department of Tsinghua University, Beijing in China. He is a Member of Engineering Professors’ Council, and a Fellow of The Higher Education Academy (HEA) in UK. He is a Senior Member of IEEE and an associate editor for IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) and IEEE Transactions on Cognitive and Developmental Systems (IEEE TCDS), and a general chair for 16th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2020).

Related Research Group(s)

Electronic Systems

Electronic Systems - Investigating processes and mechanisms found in nature to inspire alternative approaches to the design and implementation of intelligent electronic systems.