Synaptic vesicles are the substrate of neurotransmission in most nerve terminals in the central nervous system. These small membrane spheres fuse with the synaptic membrane in an activity-dependent manner and release neurotransmitter into the synaptic cleft. Subsequently, vesicles are reclaimed through endocytosis prior to reuse. This recycling process is key to supporting ongoing signalling in the brain. While substantial effort has gone into defining basic characteristics of vesicle recycling, for example elucidating the timing of vesicle turnover, key questions remain unanswered. An important area with significant knowledge deficits relates to the relationship between vesicle function and ultrastructural organisation in the terminal. The aim of this thesis is to address this issue, exploiting new methodologies which provide novel insights into function-structure relationships of vesicle populations in acute brain slices. Specifically, this study considers organisational principles of three defined vesicle pools as well as examining the impact of an established plasticity protocol on pool properties. The first results chapter, Chapter 3, outlines and validates the novel protocol used for fluorescently labelling functional recycling vesicle populations in acute rat brain slices using the vesicle-labelling dye FM1-43 and new antibody based probes (syt1-Oyster, CypHer5E). Reporter-labelling and release properties are compared to similar approaches using cultured neurons. We conclude that this approach provides a more physiologically relevant method to study the functional properties of cells than used previously in cultured neurons. Chapter 4 outlines experiments utilising the capability of FM 1-43 to be photoconverted to an electron-dense form to allow a defined vesicle population, the readily releasable pool (RRP), to be characterised ultrastructurally. The RRP is arguably the most significant pool class, released first in response to an activity train. Functional assays and time-stamped electron microscopy are used to define basic properties of this pool, including its size, functional release kinetics, and temporal organisation. Specifically, the results demonstrate that retrieved vesicles are close to the active zone after stimulation, but mixed randomly in the terminal volume over 20 min. These findings address fundamental questions about vesicle reuse, the composition of future vesicle pools, and thus the mechanism of ongoing signalling in the brain. The same approach was used in Chapter 5 to examine the influence of Long Term Depression (LTD) on pool function and ultrastructure. LTD was induced in presynaptic terminals in CA1 via Schaffer collateral activation, and the following effects were observed: 1) a change in release kinetics; 2) a reduction in the total recycling pool size; and 3) no change in the composition of the docked pool. These findings demonstrate that there is a presynaptic component to LTD and that vesicle recruitment into the recycling pool appears to be an important possible substrate. However, the results suggest that such changes appear to be selective for specific pool subsets. Overall, work in this chapter offers new insights into fundamental principles supporting synaptic plasticity. Chapter 6 expands on previous studies which have demonstrated that recycling vesicles are constitutively shared between neighbours. This sharing of a ‘superpool' of vesicles has implications for the ability of synapses to adapt to changes in input weighting. In this chapter, the methods outlined above, as well as a new 3D EM technology, are used to define the size, positional organisation, and clustering properties of this pool in native hippocampal slice system. The findings in this chapter reveal that extrasynaptic vesicles appear to show a greater degree of motility than vesicles which remain in the intrasynaptic cluster, perhaps implying differential interactions with structural proteins in the synapse. Characterising the superpool is increasingly relevant, as it is now implicated in models of plasticity and disease. Taken together, these results show that the ultrastructural arrangement of recycling vesicles is highly activity-dependent, and that the cytoarchitecture plays a large role in determining the functionality of individual vesicles and synapses.

Sustained neural activity critically relies on the ongoing function of small central synapses. In particular, activity-driven fusion and recycling of neurotransmitter-filled vesicles at presynaptic terminals are key processes responsible for information transfer. Despite the fact that vesicle exocytosis and endocytosis are of great interest, the mechanisms of their regulation are still poorly understood. Moreover, hippocampal synapses exhibit high levels of variability in their structure and function, but the basis for this remains unclear. The aim of this work was to investigate these fundamental properties and establish key rules of regulation. Specifically, we wanted to test whether the timing of endocytosis of single synaptic vesicles was characteristic at individual boutons, and to investigate structural and molecular properties of synapses that underlie their particular behaviour. To explore this, we used a variety of optical imaging techniques in rat hippocampal neurons based on acutely applied probes such as FM1-43 dye, fluorescently tagged antibodies and genetically encoded reporters of presynaptic function, as well as ultrastructural readouts using electron microscopy. We found that although the timing of vesicle retrieval, measured with the optical reporter sypHy2x, was highly variable across the population of synapses, individual boutons showed signature endocytic kinetics. We also uncovered the properties of synapses that determine this behaviour, and demonstrated that these could be modulated, leading to predictable changes in the timing of recycling. These findings offer new insights into the rules that govern the function of presynaptic terminals. A second related objective examined was whether amyloid beta, the misfolding protein implicated in Alzheimer's disease, causes changes that are detrimental for efficient vesicle recycling. We showed that oligomeric amyloid beta 1-42 impaired endocytosis and disrupted other related presynaptic processes. We suggest that vesicle recycling mechanisms are important target substrates in Alzheimer's disease providing potential new avenues for development of therapeutic approaches.

Binge alcohol drinking is a major public health concern world wide and its occurrence is rising among young adults. Using animal and human subjects, this thesis evaluates the impact of binge drinking during a time of neurodevelopment on aspects of impulse control, and studies the potential of addressing a molecular target, the μ-opioid receptor, to alleviate elevated impulsive-like behaviour. First, the nature of impulsivity is described in a review paper. We demonstrate the suitability of the Five-Choice Serial Reaction Time Task (5-CSRTT) for measuring one facet of impulsivity, waiting impulsivity, in mice. Bridging the animal and human laboratories, we developed a novel human analogue of the 5-CSRTT (paper 2). Elevated impulsive behaviour was detected in both young human binge drinkers and in an ethanol-preferring strain of mice, suggesting impulsivity to occur as a prelude to heavy alcohol use. In a second approach (paper 3), we studied the long term effects of intermittent alcohol exposure using a mouse model of adolescent binge drinking. We revealed disrupted impulsive behaviour in adulthood in two different inbred strains, which differ in baseline impulsivity and ethanol drinking patterns, indicating that impulsivity is also a consequence of ethanol exposure. In paper 4 we studied the ability of an opioid antagonist to improve top-down control of impulsive behaviour. Consilience between species and paradigms will need to be further addressed in future studies, but antagonising μ-opioid systems may aid in preventing binge drinking by facilitating inhibitory control mechanisms. Collectively, from animal and human evidence, this thesis will argue that exaggerated impulsivity may result from repeated ethanol withdrawal in adolescence as well as being a pre-existing endophenotype contributing to adolescent binge drinking. Disentangling such a relationship may help delineate new lines of intervention for at-risk individuals.

Complex networks are ubiquitous in nature. Numerous neurological diseases, such as Alzheimer's, Parkinson's, epilepsy are caused by the abnormal collective behaviour of neurons in the brain. In particular, there is a strong evidence that Parkinson's disease is caused by the synchronisation of neurons, and understanding how and why such synchronisation occurs will bring scientists closer to the design and implementation of appropriate control to support desynchronisation required for the normal functioning of the brain. In order to study the emergence of (de)synchronisation, it is necessary first to understand how the dynamical behaviour of the system under consideration depends on the changes in systems parameters. This can be done using a powerful mathematical method, called bifurcation analysis, which allows one to identify and classify different dynamical regimes, such as, for example, stable/unstable steady states, Hopf and fold bifurcations, and find periodic solutions by varying parameters of the nonlinear system. In real-world systems, interactions between elements do not happen instantaneously due to a finite time of signal propagation, reaction times of individual elements, etc. Moreover, time delays are normally non-constant and may vary with time. This means that it is vital to introduce time delays in any realistic model of neural networks. In this thesis, I consider four different models. First, in order to analyse the fundamental properties of neural networks with time-delayed connections, I consider a system of four coupled nonlinear delay differential equations. This model represents a neural network, where one subsystem receives a delayed input from another subsystem. The exciting feature of this model is the combination of both discrete and distributed time delays, where distributed time delays represent the neural feedback between the two sub-systems, and the discrete delays describe neural interactions within each of the two subsystems. Stability properties are investigated for different commonly used distribution kernels, and the results are compared to the corresponding stability results for networks with no distributed delays. It is shown how approximations to the boundary of stability region of an equilibrium point can be obtained analytically for the cases of delta, uniform, and gamma delay distributions. Numerical techniques are used to investigate stability properties of the fully nonlinear system and confirm our analytical findings. In the second part of this thesis, I consider a globally coupled network composed of active (oscillatory) and inactive (non-oscillatory) oscillators with distributed time delayed coupling. Analytical conditions for the amplitude death, where the oscillations are quenched, are obtained in terms of the coupling strength, the ratio of inactive oscillators, the width of the uniformly distributed delay and the mean time delay for gamma distribution. The results show that for uniform distribution, by increasing both the width of the delay distribution and the ratio of inactive oscillators, the amplitude death region increases in the mean time delay and the coupling strength parameter space. In the case of the gamma distribution kernel, we find the amplitude death region in the space of the ratio of inactive oscillators, the mean time delay for gamma distribution, and the coupling strength for both weak and strong gamma distribution kernels. Furthermore, I analyse a model of the subthalamic nucleus (STN)-globus palidus (GP) network with three different transmission delays. A time-shift transformation reduces the model to a system with two time delays, for which the existence of a unique steady state is established. Conditions for stability of the steady state are derived in terms of system parameters and the time delays. Numerical stability analysis is performed using traceDDE and DDE-BIFTOOL in Matlab to investigate different dynamical regimes in the STN-GP model, and to obtain critical stability boundaries separating stable (healthy) and oscillatory (Parkinsonian-like) neural ring. Direct numerical simulations of the fully nonlinear system are performed to confirm analytical findings, and to illustrate different dynamical behaviours of the system. Finally, I consider a ring of n neurons coupled through the discrete and distributed time delays. I show that the amplitude death occurs in the symmetric (asymmetric) region depending on the even (odd) number of neurons in the ring neural system. Analytical conditions for linear stability of the trivial steady state are represented in a parameter space of the synaptic weight of the self-feedback and the coupling strength between the connected neurons, as well as in the space of the delayed self-feedback and the coupling strength between the neurons. It is shown that both Hopf and steady-state bifurcations may occur when the steady state loses its stability. Stability properties are also investigated for different commonly used distribution kernels, such as delta function and weak gamma distributions. Moreover, the obtained analytical results are confirmed by the numerical simulations of the fully nonlinear system.