Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.739037
Title: Statistical approaches of investigating intrinsic mechanisms underlying spike patterning in oxytocin neurones
Author: Reiff-Marganiec, Arleta
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2003
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Abstract:
Information in the brain is carried by the temporal pattern of action potentials generated by neurones. The patterns of spike discharge are determined by intrinsic properties of each neurone and the synaptic inputs it receives. Modulation of either of these parameters changes the output of the neurone and through this the behaviour of physiology of the organism. Computational models of brain function usually focus on how patterns of connectivity contribute to information processing, but fail to take the different intrinsic properties of different neuronal phenotypes into account. Models of single neurones that take into account all intrinsic mechanisms will be extremely complex, and hence building large-scale models of neurone networks will be computationally intense, if not infeasible. In order to develop simple models that still reflect realistically the intrinsic properties of the neurone we first need to know which of the many identified mechanisms are the most important for its function. Conventionally, intrinsic properties are investigated in detail in isolated cells in vitro. Insights gained thereby are taken to speculate how these mechanisms contribute to spike patterning or neuronal responses in vivo. However, in vitro experiments are performed under artificial circumstances. Besides the relative scarcity of afferent input in dissociated cells, the preparations for the experiments require interventions that fundamentally disturb cell properties. Thus, it is problematic to interpret observations made in vivo on the basis of results obtained in vitro. In this thesis a novel, radically different way of investigation is presented. We examine recordings of firing activity of oxytocin neurones using statistical methods. The use of spontaneous, unexceptional activity recorded in vivo avoids all the interventions and alterations associated with in vitro preparations, also allowing to take the influence of afferent input into account. The main purpose of our work is to determine key features involved in the regulation of discharge patterns, and consider possible explanations in terms of known intrinsic properties. A further objective is to determine whether there are any consistent, characteristic differences in the firing pattern of oxytocin neurones recorded under a variety of physiological conditions (naive, pregnant, lactating, and hyperosmotic stimulation). We have found that while firing activity appears to be random (except for the effects of the HAP) on a small time scale (>0.5 s), on a time scale of several seconds it appears to be much more ordered. Also, we found evidence of a 'balancing' mechanism, whereby on a short to medium time scale periods of faster activity are followed by periods of slower activity (and vice versa), thus leading to a rather homogenous and steady activity overall. Of the known intrinsic mechanisms the AHP effects the firing activity in a way compatible with the firing characteristics found. Thus, from the results of the statistical analyses we conclude that the most important parameters to determine the firing of oxytocin neurones are the post-spike HAP and the post-train AHP. In addition, the analysis of the activity recorded under different physiological conditions reveal that the firing of pregnant and hyperosmotically stimulated neurones is remarkably similar to the firing in nai've organisms. In contrast, firing activity during lactation shows subtle differences indicating that the AHP decays faster in these circumstances, which is in agreement with results obtained in vitro.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.739037  DOI: Not available
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