Understanding spiking and bursting electrical activity through piece-wise linear systems

Gheorghe, Ana Maria (2012) Understanding spiking and bursting electrical activity through piece-wise linear systems. PhD thesis, University of Nottingham.

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In recent years there has been an increased interest in working with piece-wise linear caricatures of nonlinear models. Such models are often preferred over more detailed conductance based models for their small number of parameters and low computational overhead. Moreover, their piece-wise linear (PWL) form, allow the construction of action potential shapes in closed form as well as the calculation of phase response curves (PRC). With the inclusion of PWL adaptive currents they can also support bursting behaviour, though remain amenable to mathematical analysis at both the single neuron and network level. In fact, PWL models caricaturing conductance based models such as that of Morris-Lecar or McKean have also been studied for some time now and are known to be mathematically tractable at the network level.

In this work we proceed to analyse PWL neuron models of conductance type. In particular we focus on PWL models of the FitzHugh-Nagumo type and describe in detail the mechanism for a canard explosion. This model is further explored at the network level in the presence of gap junction coupling.

The study moves to a different area where excitable cells (pancreatic beta-cells) are used to explain insulin secretion phenomena. Here, Ca2+ signals obtained from pancreatic beta-cells of mice are extracted from image data and analysed using signal processing techniques. Both synchrony and functional connectivity analyses are performed. As regards to PWL bursting models we focus on a variant of the adaptive absolute IF model that can support bursting. We investigate the bursting electrical activity of such models with an emphasis on pancreatic beta-cells.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Coombes, S.
Houston, P.
Subjects: Q Science > QP Physiology > QP351 Neurophysiology and neuropsychology
Faculties/Schools: UK Campuses > Faculty of Science > School of Mathematical Sciences
Item ID: 12512
Depositing User: EP, Services
Date Deposited: 05 Nov 2012 14:27
Last Modified: 17 Dec 2017 01:32
URI: https://eprints.nottingham.ac.uk/id/eprint/12512

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