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|Durie R thesis 08.pdf||Open Access ||4.89 MB||Adobe PDF||View/Open||Durie r thesis 08 - thesis_code.zip||Original files are restricted access||132.43 MB||Unknown||Durie R thesis 08 - latex files.zip||Original files are resticted access||218.7 kB||Unknown|
|Title: ||A Population Model of Vasopressin Secretion|
|Authors: ||Durie, Ruth Frances|
|Supervisor(s): ||Leng, Gareth|
|Issue Date: ||24-Jun-2008|
|Abstract: ||Computer modelling is a powerful tool for clarifying and testing theory. In neuroscience, this often means replicating firing patterns. Models need evaluation functions to quantify the significance of features in the firing patterns, but usually the effect of firing is insufficiently understood.
The magnocellular vasopressin neurons of the hypothalamus do have an output that is both well understood and quantifiable: they secrete a hormone into the bloodstream in proportion to blood osmolarity and volume, regulating these properties within a narrow physiologically acceptable range. This response of vasopressin secretion to osmotic pressure must be maintained to defend blood pressure. The neurons display a distinctive phasic firing pattern, which a model was developed to mimic. A further, unique step was then taken of extending this model by developing a model for the effect of firing, a stimulus-secretion model. The firing pattern model and stimulus secretion model were then linked and then noisily duplicated to produce a population. This population had a measurable performance - secretion - allowing evaluation of the model in a novel fashion.
The population could replicate the secretory response to osmotic pressure observed in vivo. It is possible to test the effect of features by incorporating them into the model and observing the response. A demonstration of this was conducted by changing the mix of excitatory and inhibitory PSPs, showing that inhibition was necessary for an efficient response. Effective techniques may well be reused elsewhere in the brain, so exploring their significance in a simple system may allow understanding of more complex ones.
This project has constructed a model from firing to effect, offering novel possibilities for quantification and therefore evaluation. The main outcomes from this work are construction of a simple model system in which features can be benchmarked; that a integrate and fire model modified to include bistability can explain the firing of vasopressin neurons; that secretion could well also be controlled by a pool structure, similar to other secretory systems and that a population of these cells can produce a linear output. It has also confirmed that balanced excitatory and inhibitory input is necessary for the most efficient response. It shows that population performance is a trade-off between maximising efficiency, maintaining the secretory response over a wide dynamic range and maximising the maximum achievable secretion rate.|
|Description: ||Institute for Adaptive and Neural Computation|
|Appears in Collections:||Informatics thesis and dissertation collection|
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