The mammalian central nervous system (CNS) develops from a thin layer of neuroepithelial stem cells that form the early neural tube. Initially neuroepithelial cells generate neurons, the identity of which depends on their position of origin along the embryonic axis. Glia are generated at the end of neuronogenesis and very little is known about their specification. One of the challenges of modern developmental biology is to understand the extent of the diversity of glial cell types found in the CNS and how this diversity is generated. In this thesis I describe work using Cre-loxP technology combined with PAC transgenesis that clarifies the origins of various glial cells in the CNS. CNS glial cell generation was studied by fate-mapping restricted pools of neural precursors and identifying their glial progeny. Using PAC transgenesis technology I generated mice expressing Cre recombinase in the same patterns as the transcription factors DbxJ, Emxl and MsxS. The resulting transgenic mice were crossed with Cre-dependant reporter mouse strains to visualise the progeny of the various neuroepithelial regions. Using these techniques I was able to demonstrate that the all the neuroepithelial domains studied produce neurons as well as the major glial cell types astrocytes and oligodendrocytes. Of particular interest was the demonstration of novel sources of oligodendrocytes in the dorsal spinal cord and cortex. This work helps to resolve much of the controversy surrounding the origins of cortical and spinal cord oligodendrocytes. Investigation of the mechanism of dorsal oligodendrocyte specification indicates that dorsally-derived oligodendrocytes may be specified by Sonic hedgehog (SHH) -independent pathways, unlike their ventrally derived counterparts. The implications of these results are discussed in the context of possible mechanisms of glial cell specification in the CNS.

This thesis considers the temporal structure of burst dynamics in early brain activity through the analysis of human very preterm electroencephalograph (EEG) recordings and computational neural network models. A novel algorithm for the detection of the discontinuous bursts of activity in the preterm EEG is developed and the temporal structure of burst occurrence, size and duration are assessed. The dynamics are shown to exhibit long-range temporal correlations (LRTCs) indicating a temporal complexity within early brain activity not previously appreciated. This result is replicated in a larger population of preterm children and the effect of gestational age and postnatal age on the degree of LRTCs is examined. A possible mechanism underlying the generation of burst activity that exhibits LRTCs is investigated in a stochastic excitatory neural network model. It is shown that burst dynamics occur in the model when there is a balance between the activity of an individual neuron and the number of neurons it in turn activates. Furthermore, it is shown that correlations in the temporal statistics of these bursts exist over a wide range and extend across an infinite range in the limit of system size. The behaviour of the model with respect to different network topologies is also investigated. In summary, it is shown that complex temporal dynamics exist even in early brain activity and such dynamics can be observed in a simple model. In light of this, the evidence that the brain exhibits self-organised criticality - a theoretical framework suggested by previous authors as an explanation for LRTCs in a systems dynamics - is discussed. Overall, the observation of complex temporal structure of activity in the early developing brain suggests that the temporal organisation of this activity may play an important developmental role. This thesis therefore provides strong motivation for future work in this area.

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