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Neural stem cell (NSC) transplantation is a promising therapy for the treatment of brain damage. Although the “proof of principle” for NSC transplantation therapy has been demonstrated in a variety of animal models of brain injury (stroke, traumatic brain injury, ageing) and in a clinical setting (Parkinson’s disease), the mechanisms by which grafted stem cells survive, migrate and differentiate in host brain are yet to be elucidated. Initial studies have demonstrated that, after transplantation of the MHP36 neural stem cell line in a focal ischaemia model, the lipid transport protein apolipoprotein E (apoE) is upregulated and co-localised to differentiated cells in parallel with functional recovery. ApoE has been shown to have a critical role in the response to brain injury and repair processes. Furthermore, in humans, three different forms of apoE exist (E2, E3, E4 encoded by the alleles e2, e3, e4) and each of these has a different ability to promote repair, with the E4 form associated with an impaired capacity. This thesis tests the hypothesis that apoE is critical in stem cell integration and investigates whether this effect is APOE genotype dependent, in a mouse model of global cerebral ischaemia. This model was chosen as it produces diffuse selective neuronal damage in the striatum and hippocampus, which also occurs in other conditions such as ageing and Alzheimer’s disease. The studies described in this thesis were designed to test the hypothesis and are outlined as follows: I. Characterisation of neural stem cell grafts in a mouse model of global ischaemia In order to investigate the potential influence of apoE on stem cell grafts, it was first essential to characterise stem cells grafts in mouse brain. Thus, the initial aim of the thesis was to characterise MHP36 grafts in a mouse model of ischaemic neuronal injury. The effect of cyclosporin A (CsA) immunosuppression was also investigated. C57Bl/6J mice underwent an episode of transient global ischaemia induced by bilateral common carotid artery occlusion. Three days following ischaemia, mice received a unilateral striatal graft of fluorescently labelled MHP36 neural stem cells or vehicle; the mice also received CsA or saline. The mice were terminated at either XVII 1 or 4 weeks post-transplantation. This study determined that MHP36 grafts survived and migrated robustly in host ischaemic brain at both 1 week and 4 weeks post-transplantation. Grafted MHP36 cells differentiated into neurons and were able to reduce the extent of ischaemic neuronal damage. An acute host inflammatory response was evoked following MHP36 grafting, but this decreased dramatically by 4 weeks post-transplantation. CsA immunosuppression did not affect MHP36 survival and migration or reduce the host inflammatory response. The successful transplantation and characterisation of MHP36 grafts in mouse brain allowed for future investigation into the genetic factors underlying stem cell graft integration via the use of apoE transgenic mice. II. Influence of apoE on neural stem cell grafts in a mouse model of global ischaemia The aim of this study was to investigate whether endogenous apoE influenced MHP36 survival, migration and differentiation and then to determine potential signalling pathways that may be involved. ApoE deficient mice on a C57Bl/6J background (APOE-KO) and control wildtype C57Bl/6J (WT) mice were subjected to an episode of transient global ischaemia, as in Experiment 1. Two weeks following ischaemia, all mice received unilateral striatal and hippocampal grafts of MHP36 cells. All mice received CsA immunosuppression. Mice were terminated 4 weeks post-transplantation. MHP36 survival and migration was significantly increased in WT as compared to APOE-KO mice. In addition, neuronal differentiation was significantly increased in WT as compared to APOE-KO mice. Increased astrocytic differentiation was observed in the hippocampus, but not striatum of WT as compared to APOE-KO mice. Measurement of the levels of signalling proteins associated with cell survival, extracellular signal-regulated kinase (ERKs) and c-Jun amino-terminal kinase (JNKs) and their phosphorylated forms (pERK and pJNK), indicated selective alterations in JNK with no change in ERK in APOE-KO as compared to WT mice, suggesting that JNK may underlie the apoE effects in stem cell integration. This study demonstrated that apoE strongly influences the survival, migration and differentiation of grafted MHP36 cells and provides initial evidence for the signalling pathways involved. XVIII III. Influence of APOE genotype on neural stem cell grafts in a mouse model of global ischaemia Following the demonstration that endogenous mouse apoE has a critical role in MHP36 graft survival, migration and differentiation, this study sought to investigate whether these effects are influenced by human APOE genotype. Transgenic mice expressing human APOE-e3 or e4, (on an APOE-KO background) and a control group of APOE-KO mice underwent transient global ischaemia and two weeks later MHP36 cells were transplanted unilaterally into the striatum and hippocampus. 1 week after grafting the mice were started on a series of tests for motor balance and coordination using the rotarod, and taken for histology 4 weeks post-transplantation. MHP36 graft survival was significantly improved in APOE-e3 mice compared to APOE-KO and APOE-e4 mice. However, the migration and differentiation of MHP36 cells and motor performance of grafted mice were similar in all three APOE groups, indicating a comparable fate and functional activity within a 4 week survival time. Thus the data indicate that APOE genotype may influence cell survival with minimal effect on stem cell migration and differentiation. The data presented in this thesis demonstrate that endogenous apoE strongly influences MHP36 graft survival, migration and differentiation. Although there was minimal evidence that human APOE genotype influences cell migration and differentiation, stem cell survival was markedly improved in a human APOE-e3 allelic environment, which may affect the effectiveness of stem cells in APOE-e4 individuals.

Short-term synaptic depression is the phenomena where repeated stimulation leads to a decreased transmission efficacy. In this thesis, the impact of synaptic depression on the responses and dynamics of network models of visual processing is investigated, and the coding implications are examined. I find that synaptic depression can fundamentally change the operation of previously well - understood networks, and explain temporal nonlinearities present in neural responses to multiple stimuli. Furthermore, I show, more generally, how nonlinear interactions can be beneficial with respect to neural coding. I begin chapter 1 with a short introduction. In chapter 2 of this thesis, the behaviour of a ring attractor network is examined when its recurrent connections are subject to short term synaptic depression. I find that, in the presence of a uniform background current, the activity of the network settles to one of three states: a stationary attractor state, a uniform state or a rotating attractor state. I show that the rotation speed can be adjusted over a large range by changing the background current, opening the possibility to use the network as a variable frequency oscillator or pattern generator, and use mathematical analysis to determine an approximate maximum rotation speed. Using simulations, I then extend the network into two - dimensions, and find a rich range of possible behaviours. Processing in the visual cortex can be non - linear: the response to two objects or other visual stimuli presented simultaneously is often less than the sum of the responses to the individual objects. A maximum function has in some cases been proposed to describe these competitive interactions. More recent data has emphasised that such interactions have temporal aspects as well, namely that the response to an initially presented stimulus can suppress the response to a stimulus presented subsequently, especially if the first stimulus is presented at high contrast. Chapter 3 of this thesis will present a simple neuronal network featuring synaptic depression which can account for much of the temporal aspects of this behaviour, whilst remaining consistent with older data and models. Furthermore, it will show how this model leads to several strong predictions regarding the processing of low contrast stimuli sequences, as well suggesting a link between response latency and suppression strength. The response of the model to a structured sequence of input stimuli also appears to anticipate future stimuli, and we predict that the magnitude of this stimulus anticipation will decrease as contrast is decreased. Following on from investigating the temporal aspects of responses to stimuli pairs, in chapter 4 this thesis examines an abstract model of how coding is impacted by non - linear interactions, for both structured and unstructured stimuli spaces. I find that non- linear methods of responding to pairs of stimuli presented simultaneously can have a beneficial effect on coding capacity, with linearly combined responses generally leading to the highest decoding errors rates. This thesis goes on to examine the interplay between this models noise assumptions and the decoding performance, and finds that many of the assumptions made can be weakened without changing, qualitatively, these findings. In chapter 5, this thesis examines layered networks of noisy spiking neurons with recurrent connectivity and featuring depressing synapses. The contrast dependent latency and spike count statistics of the model are analysed and are found to be strongly dependent on the parameters of the noise. The tuning of parameters for models containing noisy IF neurons is discussed, and an information theoretic approach to tuning is outlined which successfully reproduces earlier work in which noise was tuned to linearise the response of a spiking network. The approach is applied to maximise the ability of the network to filter rapid noise transients at low contrast. I finish the thesis with a short concluding chapter.

Many optical processing systems rely critically on the availability of high performance, electrically-addressed spatial light modulators. Ferroelectric liquid crystal over silicon is an attractive spatial light modulator technology because it combines two well matched technologies. Ferroelectric liquid crystal modulating materials exhibit fast switching times with low operating voltages, while very large scale silicon integrated circuits offer high-frequency, low power operation, and versatile functionality. This thesis describes the design and characterisation of the SBS256 - a general purpose 256 x 256 pixel ferroelectric liquid crystal over silicon spatial light modulator that incorporates a static-RAM latch and an exclusive-OR gate at each pixel. The static-RAM latch provides robust data storage under high read-beam intensities, while the exclusive-OR gate permits the liquid crystal layer to be fully and efficiently charge balanced. The SBS256 spatial light modulator operates in a binary mode. However, many applications, including helmet-mounted displays and optoelectronic implementations of artificial neural networks, require devices with some level of grey-scale capability. The 2 kHz frame rate of the device, permits temporal multiplexing to be used as a means of generating discrete grey-scale in real-time. A second integrated circuit design is also presented. This prototype neuraldetector backplane consists of a 4 x 4 array of optical-in, electronic-out processing units. These can sample the temporally multiplexed grey-scale generated by the SBS256. The neurons implement the post-synaptic summing and thresholding function, and can respond to both positive and negative activations - a requirement of many artificial neural network models.

This thesis presents a Multiple Self-Organised Spiking Neural Networks (MSOSNN). The aim of this architecture is to achieve a more biologically plausible artificial neural network. Spiking neurons with delays are proposed to encode the information and perform computations. The proposed method is further implemented to enable unsupervised competitive and self-organising learning. The method is evaluated by application to real world datasets. Computer simulation results show that the proposed method is able to function similarly to conventional neural networks i.e. the Kohonen Self-Organising Maps. The SOSNN are further combined to form multiple networks of the Self-Organised Spiking Neural Networks. This network architecture is structured into <i>n</i> component modules with each module providing a solution to the sub-task and then combined with other modules to solve the main task. The training is made in such a way that a module becomes a winner at each step of the learning phase. The evaluation using different data sets as well as comparing the network to a single unity network showed that the proposed architecture is very useful for high dimensional input vectors. The Multiple SOSNN architecture thus provides a guideline for a complex large-scale network solution.

This thesis presents a way to generate a Digital Terrain Model (dtm) from a Digital Surface Model (dsm) and multi spectral images (including the Near Infrared (nir) color band). An Artificial Neural Network (ann) is used to pre-classify the dsm and multi spectral images. This in turn is used to filter the dsm to a dtm. The use of an ann as a classifier provided good results. Additionally, the addition of the nir color band resulted in an improvement of the accuracy of the classifier. Using the classifier, a dtm was easily extracted without removing natural edges or height variations in the forests and cities. These challenges are handled with great satisfaction as compared to earlier methods.

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A key component of autonomous outdoor navigation in unstructured environments is the classification of terrain. Recent development in the area of machine learning show promising results in the task of scene segmentation but are limited by the labels used during their supervised training. In this work, we present and evaluate a flexible strategy for terrain classification based on three components: A deep convolutional neural network trained on colour, depth and infrared data which provides feature vectors for image segmentation, a set of exchangeable segmentation engines that operate in this feature space and a novel, air pressure based actuator responsible for distinguishing rigid obstacles from those that only appear as such. Through the use of unsupervised learning we eliminate the need for labeled training data and allow our system to adapt to previously unseen terrain classes. We evaluate the performance of this classification scheme on a mobile robot platform in an environment containing vegetation and trees with a Kinect v2 sensor as low-cost depth camera. Our experiments show that the features generated by our neural network are currently not competitive with state of the art implementations and that our system is not yet ready for real world applications.

Multiple sclerosis (MS) is a leading cause of neurological disability in young adults. Although current treatments can reduce symptomology and relapse rate, they are unable to prevent the chronic neurodegeneration that occurs at later stages. MS pathology is mediated by complex interactions between invading immune cells, neurons, glia, and endogenous stores of neural progenitor cells (NPCs). Factors critical to NPC/immune cell communication as well as the survival, differentiation, and proliferation of NPCs are not well defined. Elucidation of these factors will allow for the advancement of NPC transplantation therapies as well as the identification of novel pharmacological targets. Fas – a member of the tumor necrosis superfamily of death receptors – has diverse, cell-specific functions and is a major modulator of autoregulation within the immune system. Although Fas is expressed by NPCs, its exact role in this cell type was previously unknown. To contribute to this body of knowledge, the experiments in this dissertation examined the role of the Fas receptor (Fas) and Fas ligand (FasL) in NPC survival, differentiation, and T-cell cross-talk in vitro and in vivo in experimental autoimmune encephalomyelitis (EAE; a well-established animal model of MS). Activation of Fas via FasL increased NPC survival by decreasing apoptosis (as opposed to increasing proliferation) in vitro. This decreased apoptosis correlates with upregulation of the inhibitor of apoptosis protein (IAP) Birc3. Further investigation into the importance of Fas in NPCs was accomplished by comparing wild-type and Fas-deficient (lpr) NPCs. Lpr NPCs exhibited decreased apoptosis, decreased proliferation, and increased differentiation to oligoprogenitor and neuronal lineages. These studies suggest the Fas system plays multifaceted roles in NPCs and that its exact functions are dependent on both functional Fas expression and presence or absence of FasL. To determine the role of Fas/FasL in neuroimmune cross-talk, co-cultures of wild-type or lpr NPCs with different T-cell subtypes (Th1, Th2, and Th17 cells) were performed. Th1 cells were the only subtype capable of inducing NPC apoptosis. Th1-mediated death was dose-dependent and was not mediated via Fas. On the other hand, NPCs were able to induce significant apoptosis in pro-inflammatory Th1 and Th17 cells without affecting anti-inflammatory Th2 cells. NPC-induced Th17 cell death was mediated via Fas. These data suggest NPCs can specifically target pro-inflammatory T-cells and can promote neuroprotection by inducing death of these proencephalogenic cells. Finally, intravenous injection of wild-type or lpr NPCs into EAE mice reduced clinical symptoms and CNS immune infiltrate to the same extent. Few NPCs enter the CNS, where they remain undifferentiated. This suggests the main mechanism through which NPCs produce beneficial results in EAE is via peripheral immunoregulation, which is not dependent on Fas expression. Overall, this dissertation elucidates the Fas system as an important modulator of NPC cell-fate and immunoregulatory capacity.

Conventional feed forward Neural Networks have used the sum-of-squares cost function for training. A new cost function is presented here with a description length interpretation based on Rissanen's Minimum Description Length principle. It is a heuristic that has a rough interpretation as the number of data points fit by the model. Not concerned with finding optimal descriptions, the cost function prefers to form minimum descriptions in a naive way for computational convenience. The cost function is called the Naive Description Length cost function. Finding minimum description models will be shown to be closely related to the identification of clusters in the data. As a consequence the minimum of this cost function approximates the most probable mode of the data rather than the sum-of-squares cost function that approximates the mean. The new cost function is shown to provide information about the structure of the data. This is done by inspecting the dependence of the error to the amount of regularisation. This structure provides a method of selecting regularisation parameters as an alternative or supplement to Bayesian methods. The new cost function is tested on a number of multi-valued problems such as a simple inverse kinematics problem. It is also tested on a number of classification and regression problems. The mode-seeking property of this cost function is shown to improve prediction in time series problems. Description length principles are used in a similar fashion to derive a regulariser to control network complexity.