Project Title: Artificial neuronal networks for multi-tasking in insect cognition and behaviour
Supervisor 1: Prof. Lars Chittka (Queen Mary, University of London)
Supervisor 2: Prof. Peter McOwan (Queen Mary, University of London)
Supervisor 3: Prof. Phil Husband (University of Sussex)
The behavioural complexity of social insects such as bees and ants has inspired generations of researchers as well as public imagination. This complexity raises two major questions: how do insects generate such complexity with so few neurons? If so much can be achieved with relatively little neuronal hardware, what are big brains for? Honeybee brains contain about 850,000 neurons (whereas human brains contain an estimated 85 billion), so a comprehensive understanding of brain function is clearly more feasible in bees than in humans. Here, the goal is to work towards minimal neural networks mimicking the entire behavioural repertoire of the honeybee, while re-using individual neurons for multiple circuits where this is realistic.
We will devise and test neural circuits grounded in current knowledge of neuroanatomy and neurophysiology. We will develop evolving artificial neural networks, with each stage inheriting knowledge from the previous one to enhance simulation efficacy and performance. Results will allow us to predict the minimum number of evolutionary changes needed to generate novel behavioural competences. The PhD student will therefore discover groundbreaking information about how behaviour could be mediated in neuronal circuit terms. The student will be trained in state-of-the-art computational neuroscience modelling. Chittka’s lab has an exceptional record with training postgraduate students at a very competitive level; all students generated very prestigious publications during their 3 years, and most have continued scientific careers at top tier institutions; many are now themselves group leaders at prestigious institutions.
You should have a first class (or high 2:1) undergraduate, or masters degree or international equivalent in Computer Science or Biology. You will need to be an excellent programmer, ideally with experience in neural network modelling or computational neuroscience.
To apply please email the following documents to Prof Lars Chittka (email@example.com): a completed application form, a CV listing all qualifications and publications, your representative publications in PDF format, a research statement and other relevant documents as requested (see www.qmul.ac.uk/postgraduate/apply/ ). These documents must also be submitted online following the instructions given in the link.