Location:
Department of Mathematics, University of Exeter. Streatham Campus, Exeter, Devon.
The University of Exeter’s College of Engineering, Mathematics and Physical Sciences is inviting applications for a fully-funded PhD studentship to commence in September 2022 or as soon as possible thereafter. The studentship will cover Home tuition fees plus an annual tax-free stipend of at least £15,609 for 3.5 years full-time, or pro rata for part-time study.
This College studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.
Project Description:
Neuroscience experiments show that each particular motor behaviour can be characterised by a set of neurons producing a pattern of electrical activity. However, the transitions between patterns are poorly understood. The wiring diagram of a neural network defines its structure and shapes its functionality (behaviour) by producing the neuronal activity patterns. This structure-function relationship is not fixed and may change dynamically on a millisecond scale, enabling rapid change of activity patterns in response to different situations.
The young frog tadpole is an ideal system to study dynamic network reconfiguration because the connectivity in its motor circuits is known. In the tadpole, such dynamic connection reconfiguration means life or death. The same motor circuits that produce swimming, to flee from predators, can also produce struggling if the tadpole is caught or stuck against an obstacle. Swimming involves a rapid rhythmic wave of muscle contractions propagating from head to tail. Struggling is a slower but more powerful rhythm that propagates from tail to head. It is critical that the tadpole quickly transitions from swimming to struggling if caught.
This project will test the hypothesis that this dynamic network reconfiguration occurs automatically in the spinal cord motor circuits as sensory inputs change, without involving neuromodulation, changes in synaptic structure, or any feedback from the brain. It will generate predictions to be tested by our experimental collaborators.
During this project, you will build mathematical and computational models of the tadpole brain and spinal cord that support swimming and struggling. This models will be based on anatomical and physiological experimental data and incorporate different types of neurons. Population level models will include a chain of interactive segments able to propagate forward and backward waves along the chain under control of sensory inputs.
Detailed models of the brain and spinal neural circuits will include several neuronal cell types, each with their own pattern of projections to other neurons. You will build on a previously published model of the swimming network and include two newly discovered cell types. All neurons of detailed models will be modelled using the Hodgkin-Huxley formalism and include a unique combination of ion channels. You will then investigate how different patterns of stimulation along the tadpole body produce different behaviours. You will then use the models to determine how changes in sensory stimulation lead to dynamic changes in the activity of the different neuron types. You will examine the role of each cell type in the transition between swimming and struggling by altering their number and properties to understand how this affects the behaviour. Finally, you will determine how each type of neurons contributes to the transition between swimming and struggling, by conducting numerical simulations of models. Your results will be tested by our experimental collaborators and you will modify your models to account for new experimental findings.
Fundamental neuronal mechanisms are highly conserved across vertebrate species. The results you will obtain using tadpole models will be applicable to more complex brain networks in mammals. Your findings will have implications beyond basic neuroscience research: they may be used to better design robots that need to navigate difficult environments without getting stuck.
Entry Requirements:
This studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.
Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology.
Computational Neuroscience, Mathematics, Computer Science
If English is not your first language you will need to have achieved at least 6.0 in IELTS and no less than 6.0 in any section by the start of the project.
Alternative tests may be acceptable (see http://www.exeter.ac.uk/postgraduate/apply/english/).
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