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Prof K Wong-Lin,
Dr Cian O'Donnell,
Dr Saugat Bhattacharyya
Monday, February 24, 2025
Competition Funded PhD Project (Students Worldwide)
About the Project
Decision-making and learning, often occurring in the face of uncertain information, are core components of cognition in humans and other organisms. The neurosciences and cognitive sciences of decision-making and learning have acquired large and complex data, which require advanced data science and artificial intelligence (AI) methods to elucidate their neural correlates. Neurobiological plausible computational models and theoretical analysis can provide further insights into their underlying neural mechanisms and computational principles.
This Ph.D. research project will focus on two aspects: (i) developing and applying advanced AI and machine learning methods on neural and behavioural data; and (ii) developing biological neural network modelling. The data for analysis and modelling will be based on openly available data, and data from our research centre and collaborators’ labs.
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] 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] Lenfesty, Bhattacharyya and Wong-Lin (In press) Uncovering dynamical equations of stochastic decision models using data-driven SINDy algorithm. Neural Computation.
[6] Collins and Shenhav (2022) Advances in modeling learning and decision-making in neuroscience. Neuropsychopharmacology, 47, 104–118.
[2] 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] Lenfesty, Bhattacharyya and Wong-Lin (In press) Uncovering dynamical equations of stochastic decision models using data-driven SINDy algorithm. Neural Computation.
[6] Collins and Shenhav (2022) Advances in modeling learning and decision-making in neuroscience. Neuropsychopharmacology, 47, 104–118.
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