"Models of mixture coding and olfactory object recognition in honeybees"
supervised by Dr. Thomas Nowotny and Dr. Jeremy Niven,
Informatics and Life Sciences,
University of Sussex, Brighton, UK.
Deadline: 5.00pm, Tuesday 31st January 2012
Start date: October 2012
Apply here:
http://www.findaphd.com/search/ProjectDetails.aspx?PJID=36161&LID=1494
*The project:*
Despite the large number of experimental studies on olfactory systems
over the last 15 years, we still do not understand how olfaction works.
Insect olfactory systems have emerged as excellent model systems for
studying the basic computational mechanisms that underlie olfactory
coding, learning and memory. The aim of this PhD project is to advance
the theory of biological olfaction with multi-scale models of the
honeybee antennal lobe (AL). The project involves combining
computational modelling, theory and experiment to develop a specific
model of the honeybee AL and use it to understand the dynamics of
information processing.
1. Building a conductance based model for the honeybee AL
Recent experiments enable us to formulate specific models of the
honeybee AL, including implementing the correct numbers of neurons,
their organization into identified glomeruli according to a
morphological 3D atlas, and correct response profiles to numerous
chemicals. Moving from generalised AL models to a specific honeybee
model will allow us to simulate the input from actual chemicals and,
therefore, to make concrete predictions about future experimental
observations. This constrains the model more tightly and makes it
falsifiable, a concept that is under-developed in computational
neuroscience to date. The developed detailed model will be simulated
using modern supercomputing methods in the form of general purpose GPU
computing.
2. Building rate models and population mean field descriptions
Once a detailed model has been formulated and implemented, we can
identify methods to reduce it to rate equations and mean field
population models allowing us to identify the dynamical structure
underlying odour information processing in the honeybee AL.
3. Investigating incoherent mixtures and odour objects
This is the core of the proposed research work and the most exciting and
novel. Recent experimental evidence indicates that millisecond
differences in the onset of odour stimuli can alter the resulting
neuronal activity in the AL and can affect behaviour. This finding
fundamentally alters our current understanding of odour processing. The
AL could emerge as the brain region responsible for odour-background
segregation based on the coherent spatio-temporal structure of the odour
plume on millisecond scale. We may even call it odour object
recognition. The work towards this objective will entail detailed models
of the odour segregation ability of the AL, including a systematic assay
of the potential network and cellular mechanisms underlying it. The
multi-scale model stack developed in (2) can then be used to identify
the underlying dynamical systems mechanisms.
The successful student will be based in the laboratory of Dr Thomas
Nowotny in the School of Informatics/Centre for Computational
Neuroscience (CCNR) and Dr Jeremy Niven in the School of Life
Sciences/CCNR at the University of Sussex. The student will also have
the opportunity to work with the laboratory of Prof. Giovanni Galizia at
the University of Konstanz, Germany.
Applicants should have a 1st/high 2.1 in computer sciences, physical
sciences or mathematics and good computer skills are required. Previous
experience in C/C++ and/or CUDA/OpenCL is a plus. A keen interest in
neural systems is essential, though direct experience is not required.
*Funding Notes:*
The South-East Biosciences Network (www.sebnet.org.uk) is advertising 33
Doctoral Studentships across the South-East of England.
Applicants for this 4-year PhD, starting in October 2012, should possess
or expect to be awarded an Upper Second or 1st Class Honours degree (or
equivalent) in a relevant subject. Studentships are available to UK
nationals and EU students who meet the UK residency requirements.
The studentship will support the student's stipend and tuition fees.
Informal enquiries to Dr Thomas Nowotny: t.nowotny@sussex.ac.uk
*References:*
1. P. Szyszka, J. Stierle, S. Biergans, T. Nowotny, C. G. Galizia.
Honeybee neurons use millisecond time-differences in stimulus coherence
for odor-object segregation. BC11 : Computational Neuroscience &
Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011,
Freiburg, Germany, 4-6 Oct (2011).
2. C. L. Buckley and T. Nowotny. Transient Dynamics between Displaced
Fixed Points: An Alternate Nonlinear Dynamical Framework for Olfaction.
Brain Research, in press (2011).
3. C. L. Buckley and T. Nowotny, Multi-scale model of an inhibitory
network shows optimal properties near bifurcation. Phys. Rev. Lett. 106:
238109 (2011).
4. M. Papadopoulou, S. Cassenaer, T. Nowotny, G. Laurent. Normalization
for Sparse Encoding of Odors by a Wide-Field Interneuron. Science, 332:
721-725 (2011).
5. J.A. Perge, J.E. Niven, E. Mugnaini, V. Balasubramanian, P. Sterling.
Why do axons differ in diameter? J. Neurosci. in press (2011).
6. P.M.V. Simões, S.R. Ott, J.E. Niven. Associative olfactory learning
in the desert locust, Schistocerca gregaria. J. Exp. Biol. 214:
2495-2503 (2011).
7. B. Sengupta, M. Stemmler, S.B. Laughlin, J.E. Niven. Action potential
energy efficiency varies among neuron types in vertebrates and
invertebrates. PLoS Comput. Biol. 6: e1000840 (2010).
--
Dr. Thomas Nowotny
RCUK Academic Fellow & Senior Research Fellow Phone: +44-1273-678593
CCNR, Informatics, Fax: +44-1273-877873
University of Sussex,
Falmer, Brighton BN1 9QJ http://sussex.ac.uk/informatics/tnowotny
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