Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432930
Title: Olfactory development models driven by population coded chemosensor input
Author: Gill, Daljeet Singh
Awarding Body: University of Leicester
Current Institution: University of Leicester
Date of Award: 2004
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
The early olfactory pathway has recently emerged as an important region in the central nervous system (CNS) in the study of neuronal development. It presents a remarkable wiring problem where millions of olfactory receptor neurons (ORNs) converge onto a few thousand points of integration in the olfactory bulb (OB). In addition, the continual turnover of ORNs allows development to be studied in adult organisms. In this thesis, the generation of a topographic map in the OB is investigated through models driven by high-density optical chemosensor arrays possessing similar properties to ORNs. The classical models of Von der Malsburg and Willshaw based on activity-dependent competition in the visual system are adapted to examine axonal targeting in the developing OB as uncovered by the above-mentioned experimental studies. The models, after exposure to relevant chemical stimuli, establish an appropriate connection scheme, and simultaneously identify the sensor types within arrays containing randomly dispersed microbeads - hence solving the so-called sensor decoding problem. Moreover, the final model also predicts a potential role for periglomerular cells in the formation of the olfactory topographic map. The data generated from the sensor arrays whilst exposed to various odours, are analysed statistically beforehand to ensure they form a suitable input to the models. In the process, both odour and sensor type discrimination analyses are demonstrated achieving high classification rates. This marks the first attempt to model the activity-dependent development of the early olfactory pathway. Furthermore, all models are driven by realistic input data to demonstrate robust performance.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.432930  DOI: Not available
Share: