About the Project
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.
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: [Email Address Removed]
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).
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