<p> The analysis of stepping motors using linear models has been simplified through identifications of the constants. Though the existing nonlinear models assuming a smooth air-gap machine may, in some cases, yield a fairly close prediction of the characteristics of a stepping motor, the models do not represent the actual motors which are essentially salient-pole motors. A new salient-pole nonlinear model is introduced. The analysis of permanent-magnet stepping motors using the nonlinear models has been simplified by assuming constant current sources. Dynamic behaviors of a permanent -magnet stepping motor are shown by phase-plane plots and step by step transient response plots.</p> <p> Computer control of stepping motors in both open-loop and closed-loop is discussed. The open-loop control has been demonstrated to be successful and a closed-loop control system using light-sensors as feedback has been designed.</p> / Thesis / Master of Engineering (MEngr)

Long-term potentiation (LTP) and depression (LTD) are activity dependent long-lasting changes in synaptic efficacy and have been proposed as mechanisms for learning and memory. Although the exact relationship of LTP and LTD to memory is not known, they do share some properties and mechanisms that relate to memory, such as the strengthening and weakening of synapses. LTP and LTD have been studied extensively in hippocampal brain-slice preparations, due to its relatively organized structure, ease of induction, and its critical function in memory storage. Less work has been done in the neocortex despite the belief that it is heavily involved in the storage of long-term memories. Activity dependent plasticity has also been demonstrated in the basal ganglia in vivo and in vitro, but the results have been somewhat inconsistent. The experiments presented in this thesis explore a novel form of neural plasticity in two excitatory pathways (corticostriatal and thalamocortical) of the basal ganglia motor loop in the intact brain in awake, freely behaving rats. In thalamocortical slice preparations, simultaneous presynaptic stimulation and postsynaptic depolarization can induce L TP in animals prior to the critical period. However the results presented in this thesis show that applied stimulation to the thalamocortical pathway failed to produce either LTP or LTD in the awake freely moving animal.Corticostriatal LTD has been shown in slice preparations following direct tetanic stimulation of the striatum. In the current experiment, cortical stimulation failed to induce LTD although there was an observable decrease in the evoked potential following low-frequency stimulation. Corticostriatal L TP has been shown to depend on the type of stimulation applied. High-frequency and theta burst stimulation produced long-lasting changes in response amplitude in the corticostriatal pathway, with theta burst stimulation appearing to be the more effective stimulation protocol for inducing LTP in both the early and late components. Paired stimulation of the substantia nigra pars compacta and cortex indicated a modulatory action of dopamine on corticostriatal synaptic plasticity. Pairing led to a stable increase in the amplitude of LTP of both early and late components. We also report that a temporal relationship exists in the striatum with respect to the release of nigral dopamine and cortical glutamate. Simultaneous stimulation produced a more robust L TP compared to the two other conditions in which there was an applied stimulation delay to either the corticostriatal or nigrostriatal pathway. The results demonstrate the mechanistic differences, not only between the thalamocortical and corticostriatal pathways, but also slice and anesthetized preparations. The results also emphasize the need for further study on mechanisms of L TP and LTD in the various excitatory and inhibitory pathways of the basal ganglia motor loop. / Thesis / Doctor of Philosophy (PhD)

This thesis is a contribution to the formalisation of the notion of an agent within the class of finite multivariate Markov chains. In accordance with the literature agents are are seen as entities that act, perceive, and are goaldirected. We present a new measure that can be used to identify entities (called i-entities). The intuition behind this is that entities are spatiotemporal patterns for which every part makes every other part more probable. The measure, complete local integration (CLI), is formally investigated within the more general setting of Bayesian networks. It is based on the specific local integration (SLI) which is measured with respect to a partition. CLI is the minimum value of SLI over all partitions. Upper bounds are constructively proven and a possible lower bound is proposed. We also prove a theorem that shows that completely locally integrated spatiotemporal patterns occur as blocks in specific partitions of the global trajectory. Conversely we can identify partitions of global trajectories for which every block is completely locally integrated. These global partitions are the finest partitions that achieve a SLI less or equal to their own SLI. We also establish the transformation behaviour of SLI under permutations of the nodes in the Bayesian network. We then go on to present three conditions on general definitions of entities. These are most prominently not fulfilled by sets of random variables i.e. the perception-action loop, which is often used to model agents, is too restrictive a setting. We instead propose that any general entity definition should in effect specify a subset of the set of all spatiotemporal patterns of a given multivariate Markov chain. Any such definition will then define what we call an entity set. The set of all completely locally integrated spatiotemporal patterns is one example of such a set. Importantly the perception-action loop also naturally induces such an entity set. We then propose formal definitions of actions and perceptions for arbitrary entity sets. We show that these are generalisations of notions defined for the perception-action loop by plugging the entity-set of the perception-action loop into our definitions. We also clearly state the properties that general entity-sets have but the perception-action loop entity set does not. This elucidates in what way we are generalising the perception-action loop. Finally we look at some very simple examples of bivariate Markov chains. We present the disintegration hierarchy, explain it via symmetries, and calculate the i-entities. Then we apply our definitions of perception and action to these i-entities.

L'acquisition autonome de representations et de fonctionnalites en robotique pose de nombreux problemes theoriques. Aujourd'hui, les systemes robotiques autonomes sont concus autour d'un ensemble de fonctionnalites. Leurs representations du monde sont issues de l'analyse d'un probleme et d'une modelisation prealablement donnees par les concepteurs. Cette approche limite les capacites d'apprentissage. Nous proposons dans cette these un systeme ouvert de representations et de fonctionnalites. Ce systeme apprend en experimentant son environnement et est guide par l'augmentation d'une fonction de valeur. L'objectif du systeme consiste a agir sur son environnement pour reactiver les representations dont il avait appris une connotation positive. Une analyse de la capacite a generaliser la production d'actions appropriees pour ces reactivations conduit a definir un ensemble de proprietes necessaires pour un tel systeme. Le systeme de representation est constitue d'un reseau d'unites de traitement semblables et utilise un codage par position. Le sens de l'etat d'une unite depend de sa position dans le reseau. Ce systeme de representation possede des similitudes avec le principe de numeration par position. Une representation correspond a l'activation d'un ensemble d'unites. Ce systeme a ete implemente dans une suite logicielle appelee NeuSter qui permet de simuler des reseaux de plusieurs millions d'unites et milliard de connexions sur des grappes heterogenes de machines POSIX. Les premiers resultats permettent de valider les contraintes deduites de l'analyse. Un tel systeme permet d'apprendre dans un meme reseau, de facon hierarchique et non supervisee, des detecteurs de bords et de traits, de coins, de terminaisons de traits, de visages, de directions de mouvement, de rotations, d'expansions, et de phonemes. NeuSter apprend en ligne en utilisant uniquement les donnees de ses capteurs. Il a ete teste sur des robots mobiles pour l'apprentissage et le suivi d'objets.

No description available.