A range of pathological behaviours associated with treatment with dopamine receptor agonists (DA) have been reported in Parkinson's disease (PD). The behaviours, known as impulse control disorders (ICD), are linked by repetitive rewarding or motivating choices despite negative consequences and include pathological gambling, compulsive shopping, binge eating and hypersexuality. This patient group provides a model for the study of the effect of both chronic DA and the interaction between DA and vulnerability mediated by cognitive mechanisms. In this thesis, I focus on PD patients with problem gambling, or compulsive shopping behaviors compared to matched PD controls without ICDs and matched healthy volunteers. I begin by outlining the methods used in this study including fMRI, reinforcement learning and model-based fMRI. I then ask whether these behaviours are associated with enhanced learning from gain feedback or impaired learning from loss feedback. As exogenous dopaminergic medications may interfere with the physiological function of phasic dopaminergic activity as a teaching signal, I use a reinforcement learning model to examine the effects of DA and the interaction of DA and vulnerability on prediction error activity as measured in the fMRI. Here, positive reinforcement is simply defined as “any event that increases the probability of a response” and does not imply hedonic value. I show a dissociation as a function of vulnerability: dopamine agonists are associated with faster reward learning in ICD patients and slower loss learning in PD controls. The effects appear to be mediated by differences in striatal prediction error activity. I then examine the effect of DA on various forms of impulsivity including impulsive choice (or the ability to delay gratification) and reaction time with increasing decision difficulty or decision conflict. I show here that DA in ICD patients are associated with greater impulsive choice and more rapid decisions compared to off DA and PD controls. ICD patients overall have faster reaction times compared to PD controls. I also show that ICD patients on DA have worse working memory function compared to PD controls on DA. I then examine the neural correlates of the impulsive choice task in healthy volunteers in an fMRI study. In the final data chapter, I assess the effects of DA on risk taking. I show that DA increases the tendency towards risk taking choices in ICD patients accompanied by a decrease in ventral striatal activity to an index of risk. Thus, DA may lead to an underestimation of risk in ICD patients by decreasing the perception of the difference between the potential for gain and the potential for loss outcomes in the risky choice. Thus, I construct a model of the ICD behavior. In a vulnerable subset of the PD population (presumably reflecting genetic, biological or psychosocial factors), DA hastens learning from rewarding outcomes emphasizing the acquisition phase of these behaviors and DA decreases the estimation of risk. DA appears to increase sensitivity towards immediate rewards over delayed rewards, an effect that may be mediated by the influence of DA on reward magnitude. These cognitive mechanisms can help explain why ICD patients choose to pursue these short term potentially rewarding risky gains despite marked negative financial losses and social and occupational consequences.

Sleep problems in babies and young children are extremely prevalent, yet until recently little attention has been paid to them by health professionals. Sleep problems have often been accepted as part of a developmental process, however research has shown that they are often not transitory, and if not treated effectively in the early years, may have long term consequences for later life. Health visitors are in an ideal position to offer treatment at a primary care level, as they are in regular contact with the families of young children. Existing research has shown that the employment of a behavioural approach is the treatment of choice for childhood sleep problems. The aims of this study were three-fold. Firstly to evaluate the efficacy of a sleep clinic run by health visitors employing behavioural techniques in the treatment of pre-school children with sleep problems. Secondly to assess what aspects of the treatment process result in the outcomes achieved; and finally to make a formal attempt to explore the influence of an improved sleeping pattern on general behaviour. Findings, using a series of n=1 studies that allowed within subject comparisons suggest that field health visitors who have received in-service training on the use of behavioural approaches are able to offer an effective service to the families of children with sleep problems. Parents identified both the behavioural and nonspecific aspects of treatment as being equally helpful, but it remains unclear what actually did help. An improvement in general behaviour was noted for all children, although this was not found to be associated with changes in sleep pattern. Due to the small number of participants, caution must be taken in generalising from the findings. The study is critically evaluated and suggestions for future research together with implications for clinical practice are discussed.

This thesis pursues a single line of enquiry: lightness, brightness, and visual illusions. In particular, it focuses on White's effect, simultaneous brightness contrast, and low-level theories that can account for both phenomenon. In the first part (Chapters 1-2), the problem-space is defined before a review of lightness and brightness theories from both low- and high-level vision. In the second part (Chapter 3), the only two low-level VI models of brightness, capable of accounting for both White's effect and simultaneous brightness contrast, are shown to be reliant on the amplification of low spatial frequency information derived for large-scale RFs, to accurately reconstruct images and account for the illusory brightness apparent in both effects. It is argued that these large-scale RFs do not exist in VI. and that the global re-weighting and re-normalisation schemes employed by these models are not constrained by the known local nature of intra-cortical connections. Hence, it was concluded that these models are not biologically plausible. In the third part (Chapter 4), the issue of recovering low spatial frequency and local mean luminance information without explicitly sampling it, is considered. The problem is formally defined in the Scale-Space framework and solved analytically. That is, an algorithm for recovering local mean-luminance (and low spatial frequencies), from the information implicit in contrast coding cells typically found in VI, is constructed, and is referred to as the Implicit Luminance Coding (ILC) model. It is argued that the ILC model is not biologically-plausible, by virtue of its global optimisation framework being unconstrained by the known local nature of intra-cortical connections. Subsequently, a new algorithm is proposed, based on a numerical approximation to the analytical solution. The biologically-plausible ILC algorithm is developed into a complete low-level model of brightness, which makes use of the information present in multiple scale channels. The model is shown to be capable of accounting for both White's effect and simultaneous brightness contrast, by means of an interplay between two independent assimilation and contrast mechanisms. The final part (Chapter 5). is concerned with the application of the model to visual phenomenon synonymous with lightness and brightness, including all known variants of White's effect and simultaneous brightness contrast, and some effects that are traditionally accounted for by appealing to mechanisms from high-level vision, thus facilitating the delineation of low-level from higher-level phenomena. The biologically-plausible ILC model is shown to be in good accordance with this experimental data. Furthermore, qualitative accounts for the temporal evolution of the filling-in process were provided and shown to be in agreement with experiment, and novel predictions as to the temporal evolution of White's effect relative to simultaneous brightness contrast are described.

Artificial adaptive systems inspired or derived from neuro-biological components and processes have shown great promise at several levels. One behaviour required for the continuous functional operation of advanced neuro-biological systems is sleep. A definitive function or purpose for sleep and of the associated phenomenology such as dreaming, remains elusive. Correspondingly there remain many unresolved issues within the domain of artificial learning systems. One such aspect that largely remains intractable is the management of experiences once learned and encoded. This is the general problem of developing a persuasive explanation or scalable strategy for the contiguous organisation of internal representation and memory within finite resources; it is from this parallel perspective in which this research is set. This research is an exploration into the cognition of sleep and dreaming in humans and animals. Positioned between sleep & dreaming research and the machine learning domain, this thesis reports on an approach to improve the latter by formulating theories emerging from the former. Recent research investigating the responsibility of sleep processes in modifying memory have shown that for the avian and mammalian brain sleep plays an important role in long term cognitive development. A set of observations are created from the current understanding of both the benefits of sleep and the processes involved, including dreaming. From these observations the first contribution of this thesis is presented; several proposals for the cognitive benefits of sleep and dreaming in aspects of perception, consolidation, scalability, generalisation and representational conceptualisation. Previous research has investigated some aspects of sleep and dreaming in relation to machine learning. These have been positioned at two extremes of the machine learning paradigm; low level, emergent behaviour of artificial neural networks or high level, directed behaviour of symbolic artificial intelligence. This is the first report of direct research into the translation of the benefits by analogous mechanisms of sleep and dreaming at a level in-between earlier research. This combination is characterised by creating a foundation for a new genre of artificial learning strategies derived directly from sleep and dream phenomenology, Oneiric Machine Learning.! Anticipatory classifier systems (ACS) represent a niche group of machine learning systems derived from the established machine learning field of learning classifier systems (LCS). ACS are capable of latent learning; learning for the reward of learning and subsequently creating an internal generalised model of the environment. This feature aligned within the LCS framework provides an ideal developmental template. A review of the latent learning background and ACS algorithmi~. detail sets the basis for several applications illustrative of the Oneiric Machine Learning approach. Empirical evidence demonstrates how an adapted ACS system can exploit a dreamlike emergent thread based on an incomplete, generalised model of the environment to reduce the number of real actions required to reach model competency. Conceptual solutions to restrictions limiting the role to which ACS/LCS systems can represent some aspects advocated by Oneiric Machine 'Learning are presented. In mitigation of these restrictions, two novel prototype systems are described; the first introduces a method of implicitly managing state generalisation by the building of concept links into the classifier rule. The second illustrates automatic state alias triggered state augmentation and off-line resolution. Although remaining under development 1 Oneiric: of or relating to dreams or dreaming. Adapted from Oneiric Behaviour (Jouvet, 1979) used to describe rapid eye movement (REM) sleep re-animation. results in these new directions present plausible systems level architectures that are in part experimentally demonstrated. Novel solutions are presented to structural and procedural problems that promote the future development of cognitive systems within the LeS framework setting a direction for future studies.

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Physiological experiments backed by biophysical models have shown that, in central glutamatergic synapses, changes in extracellular diffusivity or glutamate transporter functions exert significant influences on the excitatory transmission. Failures of transporter functions have also been related to neurological disorders. The underlying biophysical mechanisms remain poorly understood. Here, we first combine two‐photon excitation imaging with electrophysiology to estimate the diffusivity of small soluble molecules, such as glutamate in the hippocampal neuropil (area CA1). Next, we adopt time‐resolved fluorescence anisotropy imaging microscopy to establish the previously unknown instantaneous diffusivity of small molecules in the extracellular space. The result indicates that nanometer‐scale diffusivity in the brain extracellular space is 25‐30% slower than that in free medium. Accounting for this retardation may have fundamental consequences for accurate interpretation of diffusion‐limited reactions in the brain. To obtain insight into the mechanisms contributing to the excitatory signal formation, we incorporate these results in a newly developed Monte‐Carlo model of the typical environment of small excitatory synapses including unevenly distributed receptors and transporters. In addition, we build a macroscopic three‐dimensional compartmental model of the hippocampal neuropil based on available experimental data to examine the effect of transporter distribution on the extracellular landscape of glutamate. Monte‐Carlo simulations show to what extent altering diffusivity inside or outside the synaptic cleft affect synaptic responses. Modelling also predicts that extrasynaptic transporters have little effect on fast synaptic transmission through AMPARs and NMDARs. However, they influence the responses of high‐affinity extrasynaptic receptors, such as NMDA or metabotropic receptors. Conversely, intra‐cleft glutamate transporters should significantly attenuate activation of synaptic transmission. On a larger neuropil scale, failure of >95% transporters is required for any significant elevation of glutamate (above 1‐2 μM) to occur. Our data shed light on fundamental biophysical constraints important for a better understanding of excitatory signal formation in central neural circuits.

The simple relationship of movement to position via temporal integration helps to explain some of the neural representations of position seen in the mammalian brain, such as the representations of eye-position, and of head-direction and self-location within an environment. The positional information also comes from perception, such as vision, and the two sources need not necessarily agree. I construct neuronal firing rate models (introduced in Chapter 2) that utilize both velocity and visual inputs, and test them against physiological data acquired in situations when the two inputs are put in conflict. I start with a model of the oculomotor system (Chapter 3), in which a visual target and integration of the motion signal play distinct roles. The model represents a continuous attractor, stable and unstable regimes of which are analyzed with the latter found to correspond to different clinical disorders. In Chapters 4 to 6 continuous attractors are used to model hippocampal systems for the representation of the animal's location within its environment. Chapter 4 describes the 'standard' model of the integration of self-motion information to maintain a representation of current location in the firing of hippocampal 'place cells'. I demonstrate the stability and invariance under translation of this representation under the model. Using this model, I then consider how abstracted sensory information concerning environmental location is combined with self-motion information to provide the representation of location (Chapter 5). The model is tested by simulation of experimental data on place cell firing in situations where both types of information are put into conflict. Chapter 6 investigates whether the integration of self-motion and environmental information into a single coherent representation could result from a reciprocal interaction between place cells and the recently discovered 'grid cells' in Entorhinal cortex. In this model, integration of self-motion occurs between grid cells and projections from grid cells to place cells provide the self-motion contribution to place cell firing. Conversely, sensory inputs contribute to place cell firing and projections from place cells to grid cells maintain the stability of grid cell firing relative to the environment. This model is tested against experimental data on both place cell firing and grid cell firing in situations where environmental and self-motion information are put into conflict. Testable predictions for future experimental studies are generated by the model. In Chapter 7 we discuss the relationship of our findings to other related approaches, and their implications for the neural organization of spatial behavior.

The central nervous system is plastic, in that the number and strength of synaptic connections changes over time. In the adult the most important driver of such changes is experience, in the form of learning and memory. There are thought to be a number of rules, operating relatively local to each synapse that govern changes in strength and organisation. Some of these such as Hebbian plasticity or plasticity following repeated activation of a connection have been studied in detail in animal preparations. However, recent work with non-invasive methods of transcranial stimulation in human, such as transcranial magnetic stimulation, has opened the opportunity to study similar effects in the conscious human brain. In this thesis I use these methods to explore some of the presumed changes in synaptic connectivity in the motor cortex during different forms of motor learning. The experiments only concern learning in the healthy brain; however it seems likely that the same processes will be relevant to neurorehabilitation and disease of the nervous system. This thesis explores the link between neuroplasticity and motor learning in humans using non-invasive brain stimulation, pharmacological agents and psychomotor testing in 6 related studies. 1) Chapter 3 reports initial pharmacological investigations to confirm the idea that some of the long term effects of TMS are likely to involve LTP-like mechanisms. The study shows that NMDA agonism can affect the response to a repetitive form of TMS known as theta burst stimulation (TBS) 2) Following up on the initial evidence for the role of NMDA receptors in the long term effects of TBS, Chapter 4 explores the possible modulatory effects of dopaminergic drugs on TBS. 3) Chapter 5 takes the investigations to normal behaviours by examining how the NMDA dependent plasticity produced by TBS interacts with learning a simple motor task of rapid thumb abduction. The unexpected results force a careful examination of the possible mechanisms of motor learning in this task. 4) Chapter 6 expands on these effects by employing a battery of TMS methods as well as drug agents to examine the role of different intracortical circuits in ballistic motor learning. 5) Chapter 7 studies the plasticity of intracortical circuits involved in transcallosal inhibition. 6) Chapter 8 studies the interaction between synaptic plasticity invoked by TBS and sequence learning. The studies described in the thesis contribute to understanding of how motor learning and neuroplasticity interact, and possible strategies to enhance these phenomena for clinical application.