Professor Andy Philippides
Professor Andy Philippides
Professor Of Biorobotics
Telephone: 01273 678129
Email: andrewop@sussex.ac.uk
Active Visual Learning
Project co-supervised by Prof Paul Graham
Perception is not passive. Animals actively explore their environments to acquire the information they need to guide future behaviour and evolution has shaped the animal movements to shape incoming information to make it easier to learn/recall. This is seen across animals from the choreographed learning flights of bees to eye movements of humans. However, precisely how active vision is co-evolved with task, environment and visual system is not well understood.
Hymenoptera (bees, wasps and ants) are an excellent model system in which to study this behaviour as they exhibit active learning scaffolded by evolved behaviours during visual learning and navigation. Indeed, this combination means that desert ants can learn long routes through complex terrain with single trial learning, despite having brains of only 1M neurons and low-resolution vision. By combining biological experiments with robotic and AI models, we developed an action-based model for ant route navigation and learning, which has provided new insights into small-brained cognition.
Building on this work, students will explore active visual learning experimentally, theoretically or both, with applications ranging from insects to humans, and AI to robots. The project will therefore suit students with a biological background interested in learning computational methods, or with a technical background interested in studying biology.
As well as this project area, I have collaborated widely across Sussex Neuroscience (e.g. Jeremy Niven, Kevin Staras, Chris Buckley, Catherine Hall, Chris Bird, Hugo Critchley) on projects ranging from modelling through data analysis to experimental design and would be happy to guide students doing a rotation which requires computational expertise.
Key references
- Amin, A., Philippides, A., & Graham, P. (2025). Ant visual route navigation: How the fine details of behaviour promote successful route performance and convergence. PLOS Computational Biology, 21(9), e1012798.
- Baddeley, B., Graham, P., Philippides, A., & Husbands P. (2012). A Model of Ant Route Navigation Driven by Scene Familiarity [PDF 2.4MB]. PLOS Computational Biology 8(1), e1002336.
- Collett, T., Graham, P., & Heinze, S. (2025). The neuroethology of ant navigation. Current Biology, 35(3), R110-R124.
- Graham, P., & Philippides, A. (2017). Vision for navigation: What can we learn from ants? Arthropod Structure & Development, 46(5), 718-722.
Visit the Insect Navigation Group pages for more details and a full list of publications.