معرفی
Dr James Bennett is an Assistant Professor in Computer Science & AI (Informatics) at the School of Engineering and Informatics, University of Sussex, where he has been a faculty member since 2023. Prior to this, he held postdoctoral research positions at the University of Sheffield (2022–2023), University of Sussex (2017–2022), and the University of Oxford (2014–2017). His academic background spans physics, neuroscience, and AI, reflecting his interdisciplinary research approach.
- MPhys in Physics, University of Warwick (2004–2008)
- MSc in Neuroscience, University of Oxford (2008–2009)
- DPhil in Neuroscience, University of Oxford (2009–2014)
His research lies at the intersection of biological and artificial neural systems, focusing on how insect brains—especially the Drosophila mushroom body—implement learning algorithms akin to those in machine learning. He models reinforcement learning mechanisms in biological circuits and applies neural principles to improve artificial intelligence. His work is highly relevant to sustainable development, particularly biodiversity and life on land.
The recent publications show a consistent focus on modeling neural learning in insect brains, particularly reinforcement and reward prediction errors, pattern formation, and plasticity. These studies bridge computational neuroscience and AI, using biologically plausible models to inform machine learning.
Dr Bennett teaches advanced courses in machine learning and neural networks at both undergraduate and postgraduate levels, contributing to the education of future AI and computer science specialists.
He has been active in collaborative research, with co-authors including Thomas Nowotny, Andrew Philippides, and Wyeth Bair. While no formal awards are listed, his work has been cited over 100 times and disseminated through major preprint and open-access platforms.
Dr Bennett supervises no listed students yet, but his research group likely involves collaborative projects within the informatics and neuroscience communities. His ORCID is 0000-0002-9474-426.
