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Prof K Wong-Lin,
Dr Cian O'Donnell,
Prof Girijesh Prasad
Monday, February 24, 2025
Competition Funded PhD Project (Students Worldwide)
Londonderry
United Kingdom
Artificial Intelligence
Data Science
Machine Learning
Neuroscience
Computer Science
About the Project
Decision-making and learning are core components of cognition not only in humans, but also in artificial intelligence (AI). The latter is particularly so in machine learning (ML) such as deep learning.
Existing neurobiological plausible computational models of decision-making and learning may potentially offer fresh insights and computational principles to developing novel AI technologies, and perhaps even resolve some of their current limitations.
This Ph.D. research project will focus on two aspects: (i) extracting computational principles from existing computational models of decision-making and learning; and (ii) developing novel AI/ML algorithms and technologies. The developed AI/ML algorithms and technologies will be applied to real-world data and tested against state-of-the-art approaches.
This project is available in the Computer Science Research Institute and is tenable in the Faculty of Computing, Engineering and the Built Environment, at the Magee Campus.
The successful Ph.D. candidate will benefit from the expertise of Ulster University’s Cognitive and Computational Neuroscience, Neurotechnology, AI, Machine Learning and Computational Biology communities, and will interact closely with various leading international collaborators. The student will gain valuable knowledge in AI and machine learning techniques, computational modelling, high-performance computing, applications of mathematics/statistics, and the brain sciences. This training will provide wide opportunities for finding skilled work in academia or industry, especially in the burgeoning field of AI, data science/analytics and neuroscience.
In 2021, Ulster University was ranked 2nd in the UK for Ph.D. researcher satisfaction, 6th largest Computer Science and Informatics unit, and 7th for the level of world-leading or internationally excellent research and impact with respect to staff number.
References
[1] Zador (2024) The Transmitter https://doi.org/10.53053/HTHN7530.
[2] Zador et al. (2023) Catalyzing next-generation Artificial Intelligence through NeuroAI. Nature Communications, 14(1):1597. doi: 10.1016/j.neunet.2021.09.018.
[3] O’Connell, Shadlen, Wong-Lin and Kelly (2018) Bridging neural and computational viewpoints on perceptual decision-making. Trends in Neurosciences, 41(11):838-852.
[3] Atiya, Rañó, Prasad and Wong-Lin (2019) A neural circuit model of decision uncertainty and change-of-mind. Nature Communications, 10(1):2287. doi: 10.1038/s41467-019-10316-8.
[4] Wong and Wang (2006) A recurrent network mechanism of time integration in perceptual decision decisions. The Journal of Neuroscience, 26(4):1314-1328.
[5] Collins and Shenhav (2022) Advances in modeling learning and decision-making in neuroscience. Neuropsychopharmacology, 47, 104–118.
[6] Hassabis, Kumaran, Summerfield, Botvinick (2017) Neuroscience-Inspired Artificial Intelligence. Neuron, 95(2):245-258.
[7] Macpherson et al. (2021) Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research. Neural Networks, 144:603-613.
[2] Zador et al. (2023) Catalyzing next-generation Artificial Intelligence through NeuroAI. Nature Communications, 14(1):1597. doi: 10.1016/j.neunet.2021.09.018.
[3] O’Connell, Shadlen, Wong-Lin and Kelly (2018) Bridging neural and computational viewpoints on perceptual decision-making. Trends in Neurosciences, 41(11):838-852.
[3] Atiya, Rañó, Prasad and Wong-Lin (2019) A neural circuit model of decision uncertainty and change-of-mind. Nature Communications, 10(1):2287. doi: 10.1038/s41467-019-10316-8.
[4] Wong and Wang (2006) A recurrent network mechanism of time integration in perceptual decision decisions. The Journal of Neuroscience, 26(4):1314-1328.
[5] Collins and Shenhav (2022) Advances in modeling learning and decision-making in neuroscience. Neuropsychopharmacology, 47, 104–118.
[6] Hassabis, Kumaran, Summerfield, Botvinick (2017) Neuroscience-Inspired Artificial Intelligence. Neuron, 95(2):245-258.
[7] Macpherson et al. (2021) Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research. Neural Networks, 144:603-613.
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