Connectionists: PhD in Computational and Mathematical Neuroscience (EPSRC funded, 3.5 yrs, Sept 2019 start)

[James Rankin]

*PhD in Computational and Mathematical Biology (EPSRC funded, 3.5 yrs, Sept
2019 start) [re-advertised]*

General information & apply (funding is primarily targeted at UK students):
http://www.exeter.ac.uk/studying/funding/award/index.php?id=3386

Project description:
http://www.exeter.ac.uk/codebox/phdprojects/Tabak-EPSRC-DTP-Project.pdf
<http://www.exeter.ac.uk/codebox/phdprojects/Rankin-EPSRC-DTP-Project.pdf>

*Closing date 13 May 2019.  Interviews will be held at the University of
Exeter Streatham Campus during the week of 10 June 2019.*

Neurons and endocrine cells generate pulses of electrical activity. The
patterns of electrical activity vary from cell-to-cell, and characteristics
of these patterns (pulse duration, amplitude, timing) are critical to the
function performed by the cell. For instance, electric pulse duration and
frequency determines the quantity of hormone released by endocrine cells.
Electrical currents are produced by ion channels, which act as non-linear
electric conductances. The interactions between these non-linear
conductances generate the complex patterns of electrical activity. Thus,
the exact activity pattern of a given cell depends in a complex way on the
exact distribution, or combination of weightings, of the different ion
channels.

To understand how different combinations of ion channel weightings result
in a given pattern of electrical activity, we use mathematical models based
on nonlinear differential equations that describe how the conductances vary
with the cell electric potential and how they in turn change this
potential. The problem with these models is that a lot of parameters are
unknown, including the weightings of each channel. Electrophysiologists can
record the electrical activity patterns from individual cells, but they
cannot measure all the channel weightings from one cell. The objective of
this funded PhD project is to extract information about the weightings from
the measured electrical activity.

The successful candidate will generate a database of channel weighting
combinations and compute the resulting electrical activity pattern
associated with each combination. They will then apply machine learning
and/or topological data analysis to this database. This will allow them to
deduce the relationships between weightings across models that generate
electrical activity patterns selected from a subset of the database. By
identifying the relationships between channel weightings across a highly
heterogeneous population we can pinpoint the rules that regulate electrical
activity across heterogeneous populations of cells. These methods will then
be applied to existing datasets of electrophysiological recordings from
electrically active cells.

This project provides a unique opportunity to develop experience in machine
learning and to receive training in mathematical modelling of neurons. This
work will be done in close collaboration with experimentalists using
cutting-edge methods that incorporate modelling and electrical recordings
together. The student will therefore be exposed to multidisciplinary
teamwork. If they so desire, they will also have the opportunity to learn
electrophysiology and generate their own dataset of experimental electrical
recordings.

Candidates with quantitative backgrounds (mathematics, physics,
engineering, computer science) are encouraged to apply to this 3.5 year PhD
Scholarship. Programming experience, knowledge of dynamical systems theory
and experience in biological modelling are a plus. The successful candidate
will also be expected to travel to conferences to present their work.

*For further information, please contact Dr Joel Tabak J.Tabak at exeter.ac.uk
<J.Tabak at exeter.ac.uk> and Dr James Rankin j.a.rankin at exeter.ac.uk
<j.a.rankin at exeter.ac.uk>*

http://emps.exeter.ac.uk/mathematics/staff/jar226
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