Mapping biological networks of the human brain remains one of the complex endevaours in human history. Connectomics is a field of study that aims to map all neural connections in the brain (estimated to be 100 trillion) using MRI, fMRI and EM images obtained from various projects under the Human Connectome Project. Unfortunately, these microscopic neuroimages come in wide variety of sizes, rotations and colours. Spliced 2D images of various segments of the brain with non-standardised fluorescent labelling have to be assembled into a three dimensional representation, in order to understand the neural connectivity (comprising of billions of neurons). To ensure high labelling accuracy, manual tracing (by neuroscientists) remains the gold standard in deciphering these neural connections. This is a time-consuming, trivial and labor-intensive task especially when we consider the number of plausible connections at the microscale resolution.
A citizen scientist initiative to gamify this annotation process was launched in 2012 (Eyewire) with the aim of mapping the retinal neurons. The game has attracted more than 100,000 participants from all over the world. Gamers are tasked with tracing a neuron and its connections based on images of brain slices obtained from a brain tissue. The reconstruction of this branch is validated by experts for its accuracy. Concurrently, the human tracing behaviour is adapted to an AI engine in an effort to improve the automatic annotation capability. Although the activity is simplistic and does not require prior scientific knowledge, this is not your average colouring exercise (i.e., where keeping it inside the line would suffice). There is a minimal training required before your work can be accepted.
On the one hand, the task seems trivial and is highly suited for machine learning adoption, however, the intricacies of tracing the path interconnecting these neurons require innate human traits unknown to conventional supervised learning algorithms. The amount of unannotated data further limits the capability of supervised machine learning approaches for this task. On the other hand, reinforcement learning (RL) might provide a better alternative. Rapid progress has been achieved in developing computer gamers through utilisation of deep neural networks trained using RL paradigms. Advanced RL approaches exemplified by AlphaGo Zero (and its variants) and OpenAI have demonstrated that these systems outperform the best human players in their specialised domains (e.g., Go, Chess, Shogi, 3D games, and other RPG computer games).
RL learns by competing against another agent (could be human or another machine) and in principle does not rely on the availability of labelled data for training. Is it possible to extend the capability of RL to map the human brain? The ability to accurately map our brain carries a significant impact for the future of mankind. Notwithstanding this grand contribution, the proposed research will contribute to further understanding in the area of neuroscience and may result in the development of newer approaches in AI and data analytics towards artificial general intelligence.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
Friday 7 February 2020
23 to 24 March 2020
A key player in the economy of the north west
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