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Small-world networks for Neuromorphic Computing

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  • Full or part time
    Dr Manjunathaiah
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Project Overview:
The brain is a massively parallel information processing system organised as a multi-scale network. Modelling the brain using graph theoretic framework enables the investigation of how complex behaviours arise from dynamic network organisation. Such studies also have the potential to provide insights into how diseases and disorders affect the brain. The brain network structure exhibits small-world topological properties characterized by dense local clustering and sparse long-range connections. These topological organisations are considered to be advantageous as they optimize wiring costs and thus deliver cost-effective information processing.

The principle of neuromorphic computing is to invert the ‘brain as a computer’ metaphor and design computers that closely resemble the brain in structure and behavioural properties. The premise is that in attempting to design such a system with underlying principles of low energy, network adaptability, fault tolerance and learning it may provide insights into the computational model of the brain itself.

The aim of this research is to investigate small-world network models for neuromorphic computing. Depending on the interest of study the research can be focussed on any of the related topics: neuromorphic architectures, graph metrics for network community structures, expander graphs, routing algorithms, learning algorithms.


School of Systems Engineering, University of Reading:
The University of Reading is one of the UK’s 20 most research-intensive universities and among the top 200 universities in the world. Achievements include the Queen’s Award for Export Achievement (1989) and the Queen’s Anniversary Prize for Higher Education (1998, 2006 and 2009). This project will take place in the School of Systems Engineering (SSE), which has a strong reputation for its innovative research in computer science, cybernetics, and electronic engineering.


Eligibility:
Applicants should have a bachelors (at least 2.1 or equivalent) or masters degree in Computer Science or a strongly related discipline. Strong programming and mathematical skills are preferable. Experience in any field related to the research such as parallel computing, graph theory and machine learning are desirable.


How to apply:
(1) Submit an application for a PhD in Computer Science using the link below.
(2) After submitting your application you will receive an email to confirm receipt; email should be forwarded along with a covering letter and full CV to Dr M. Manjunathaiah ([email protected])
(3) In the online application system, there is a section for “Research proposal” and a box that says “If you have already been in contact with a potential supervisor, please tell us who” – in this box, please enter “Dr M. Manjunathaiah”.


Further enquiries:
Dr M. Manjunathaiah, tel: +44(0)118 9316055 email: [email protected]

Funding Notes

We welcome applications from self-funded students worldwide for this project.

Students from Brazil: we welcome and support applications for the Science Without Borders Scholarship (Ciência sem Fronteiras).

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