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Professor Orchard's main research interest is computational neuroscience, using mathematical models and computer simulations of neural networks to understand how the brain works. Guided by both theory and anatomy, he is building large-scale neural networks based on computational theories of the brain, such as predictive coding, to uncover the way we perceive the world. His research also includes projects in decision-making network models of the basal ganglia, unsupervised learning of vision networks, spatial navigation, and population coding. He is a core member of the Centre for Theoretical Neuroscience.
Professor Orchard has also done research in the area of image processing and medical imaging. He developed a method to register (align) several images simultaneously; this is a departure from the conventional two-at-a-time registration. He has also published a number of other papers on image registration, image reconstruction for MRI and CT, denoising, image mosaicking, and forensic image processing
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arxiv(2023)
Frontiers in neuroscience (2023): 1190515
ICONS '23: Proceedings of the 2023 International Conference on Neuromorphic Systemspp.1-7, (2023)
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Journal of computational vision and imaging systemsno. 1 (2021): 1-3
arxiv(2021)
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