The work in this thesis concerns mechanisms of excitability of neurons. Specifically, it deals with how neurons respond to input, and how their response is controlled by ion channels and other active components of the neuron. I have studied excitability in two systems of the nervous system, the hippocampus which is responsible for memory and spatial navigation, and the peripheral C–fibre which is responsible for sensing and conducting sensory information to the spinal cord. Within the work, I have studied the role of excitability mechanisms in normal function and in pathological conditions. For hippocampus the normal function includes changes in excitability linked to learning and memory. However, it also is intimately linked to pathological increases in excitability observed in epilepsy. In C–fibres, excitability controls sensitivity to responses to stimuli. When this response becomes enhanced, this can lead to pain. I have used computational modelling as a tool for studying hyperexcitability in neurons in the central nervous system in order to address mechanisms of epileptogenesis. Epilepsy is a brain disorder in which a subject has repeated seizures (convulsions) over time. Seizures are characterized by increased and highly synchronized neural activity. Therefore, mechanisms that regulate synchronized neural activity are crucial for the understanding of epileptogenesis. Such mechanisms must differentiate between synchronized and semi synchronized synaptic input. The candidate I propose for such a mechanism is the fast outward current generated by the A-type potassium channel (KA). Additionally, I have studied the propagation of action potentials in peripheral axons, denoted C–fibres. These C–fibres mediate information about harmful peripheral stimuli from limbs and organs to the central nervous system and are thereby linked to pathological pain. If a C–fibre is activated repeatedly, the excitability is altered and the mechanisms for this alteration are unknown. By computational modelling, I have proposed mechanisms which can explain this alteration in excitability. In summary, in my work I have studied roles of particular ion channels in excitability related to functions in the nervous system. Using computational modelling, I have been able to relate specific properties of ion channels to functions of the nervous system such as sensing and learning, and in particular studied the implications of mechanisms of excitability changes in diseases. / <p>QC 20102423</p>

The lungs are particularly vulnerable to acute, critical illness. Respiratory failure can result not only from primary lung pathology, such as pneumonia, but also as a secondary consequence of heart failure or inflammatory illness, such as sepsis or trauma. When this occurs, it is essential to support patients with mechanical ventilation while the fundamental disease process is addressed. The goal of mechanical ventilation is to ensure adequate ventilation, which involves a magnitude of gas exchange that leads to the desired blood level of carbon dioxide, and adequate oxygenation that ensures organ function. Achieving these goals is complicated by the fact that mechanical ventilation can actually cause acute lung injury, either by inflating the lungs to excessive volumes or by using excessive pressures to inflate the lungs. Thus, the challenge to mechanical ventilation is to produce the desired blood levels of carbon dioxide and oxygen without causing further acute lung injury. In this research, we develop an analysis and control synthesis framework for a pressure-limited respirator and lung mechanics system using compartment models. Specifically, a general mathematical model is developed for the dynamic behavior of a multicompartment respiratory system. Then, based on this multicompartment model, an optimal respiratory pattern is characterized using classical calculus of variations minimization techniques for inspiratory and expiratory breathing cycles. Furthermore, model predictive controller frameworks are designed to track the given optimal respiratory air flow pattern while satisfying control input amplitude and rate constrains.