An empirical study of neighbourhood decay in Kohonen\u27s self organizing map

In this paper, empirical results are presented which suggest that size and rate of decay of region size plays a much more significant role in the learning, and especially the development, of topographic feature maps. Using these results as a basis, a scheme for decaying region size during SOM training is proposed. The proposed technique provides near optimal training time. This scheme avoids the need for sophisticated learning gain decay schemes, and precludes the need for a priori knowledge of likely training times. This scheme also has some potential uses for continuous learning

Seasonality of circulation in southern Africa using the Kohonen self-organising map

Bibliography: leaves 77-84.A technique employing the classification capabilities of the Kohonen self-organising map (SOM) is introduced into the body of computer-based techniques available to synoptic climatology. The SOM is one of many types of artificial neural networks (ANN) and is capable of unsupervised learning or non-linear classification. Components of the SOM are introduced and an application is then illustrated using observed daily sea level pressure (SLP) from the Australian Southern Hemisphere data set. To put the technique in the context of global climate change studies, a further example using simulated SLP from the GENESIS version 1.02 General Circulation Model (GCM) is illustrated, with the emphasis on the ability of the technique to highlight differences in seasonality between data sets. The SOM is found to be a robust technique for deducing the modes of variability of map patterns within a circulation data set, allowing variability to be expressed in terms of inter and intra-annual variability. The SOM is also found to be useful for comparing circulation data sets and finds particular application in the context of global climate change studies

The Development of Bio-Inspired Cortical Feature Maps for Robot Sensorimotor Controllers

Full version unavailable due to 3rd party copyright restrictions.This project applies principles from the field of Computational Neuroscience to Robotics research, in particular to develop systems inspired by how nature manages to solve sensorimotor coordination tasks. The overall aim has been to build a self-organising sensorimotor system using biologically inspired techniques based upon human cortical development which can in the future be implemented in neuromorphic hardware. This can then deliver the benefits of low power consumption and real time operation but with flexible learning onboard autonomous robots. A core principle is the Self-Organising Feature Map which is based upon the theory of how 2D maps develop in real cortex to represent complex information from the environment. A framework for developing feature maps for both motor and visual directional selectivity representing eight different directions of motion is described as well as how they can be coupled together to make a basic visuomotor system. In contrast to many previous works which use artificially generated visual inputs (for example, image sequences of oriented moving bars or mathematically generated Gaussian bars) a novel feature of the current work is that the visual input is generated by a DVS 128 silicon retina camera which is a neuromorphic device and produces spike events in a frame-free way. One of the main contributions of this work has been to develop a method of autonomous regulation of the map development process which adapts the learning dependent upon input activity. The main results show that distinct directionally selective maps for both the motor and visual modalities are produced under a range of experimental scenarios. The adaptive learning process successfully controls the rate of learning in both motor and visual map development and is used to indicate when sufficient patterns have been presented, thus avoiding the need to define in advance the quantity and range of training data. The coupling training experiments show that the visual input learns to modulate the original motor map response, creating a new visual-motor topological map.EPSRC, University of Plymouth Graduate Schoo

Improved Methods for Cluster Identification and Visualization

Self-organizing maps (SOMs) are self-organized projections of high dimensional data onto a low, typically two dimensional (2D), map wherein vector similarity is implicitly translated into topological closeness in the 2D projection. They are thus used for clustering and visualization of high dimensional data. However it is often challenging to interpret the results due to drawbacks of currently used methods for identifying and visualizing cluster boundaries in the resulting feature maps. In this thesis we introduce a new phase to the SOM that we refer to as the Cluster Reinforcement (CR) phase. The CR phase amplifies within-cluster similarity with the consequence that cluster boundaries become much more evident. We also define a new Boundary (B) matrix that makes cluster boundaries easy to visualize, can be thresholded at various levels to make cluster hierarchies apparent, and can be overlain directly onto maps of component planes (something that was not possible with previous methods). The combination of the SOM, CR phase and B-matrix comprise an automated method for improved identification and informative visualization of clusters in high dimensional data. We demonstrate these methods on three data sets: the classic 13- dimensional binary-valued “animal” benchmark test, actual 60-dimensional binaryvalued phonetic word clustering problem, and 3-dimensional real-valued geographic data clustering related to fuel efficiency of vehicle choice

Development of Some Efficient Lossless and Lossy Hybrid Image Compression Schemes

Digital imaging generates a large amount of data which needs to be compressed, without loss of relevant information, to economize storage space and allow speedy data transfer. Though both storage and transmission medium capacities have been continuously increasing over the last two decades, they dont match the present requirement. Many lossless and lossy image compression schemes exist for compression of images in space domain and transform domain. Employing more than one traditional image compression algorithms results in hybrid image compression techniques. Based on the existing schemes, novel hybrid image compression schemes are developed in this doctoral research work, to compress the images effectually maintaining the quality

Scale-freeness and small-world phenomenon in information-flow graphs of geometrical neural networks

In this dissertation we set out to study a simplified model of activation flow in artificial neural networks with geometrical embedding. The model provides a mathematical description of abstract neural activation transfer in terms, which bear resemblances to multi-value Boltzmann-like evolution. The activation-preserving constraint mimics a critical regime of the dynamics and, along with accounting for geometrical location of the neurons, makes the system more feasible for modelling of real-world networks. We focus on scale invariance or scale-freeness and small-world phenomena in the said networks. Our results clearly confirm presence of both features at the functional level of the activity-flow graph. We show that the degree distribution preserves a power-law shape with the exponent value approximately equal to -2. In addition, we present our results concerning characteristic path length in the said graphs, which grows roughly logarithmically with the size of the network, while the clustering coefficient turns out to be relatively high. Taken together, the clustering and path length ratios are surprisingly high, and thus confirm large both local and global efficiency of the network. Finally, we compare the properties of activation-flow model to those reported in neurobiological analyses of brain networks recorded with functional magnetic resonance imagining (fMRI). There is a strong agreement between the shape and exponent value of degree distribution also the clustering and characteristic path lengths are comparable in both the model and medical data.Celem niniejszej rozprawy jest analiza uproszczonego modelu przepływu aktywności w sztucznych sieciach neuronowych zanurzonych w przestrzeni geometrycznej. Przedstawiony model dostarcza matematycznego opisu transferu aktywności w terminach zbliżonych do wielowartościowych maszyn Boltzmanna. Wymóg zachowania stałej sumarycznej aktywności odzwierciedla krytyczność dynamiki i wraz z uwzględnieniem wpływu lokalizacji geometrycznej neuronów sprawia, że system jest bardziej adekwatny do modelowania rzeczywistych sieci. Badania koncentrują się na bezskalowości oraz fenomenie małego świata w wyżej wymienionych sieciach. Uzyskane rezultaty potwierdzają obecność obu własności w omawianych grafach. Pokażemy, że rozkład stopni wejściowych wierzchołków zachowuje się jak funkcja potęgowa z wykładnikiem równym -2. Ponadto prezentujemy wyniki dotyczące charakterystycznej długości ścieżki, który rośnie logarytmicznie wraz z wielkością systemu, podczas gdy współczynnik klasteryzacji okazuje się dość duży. W konsekwencji stosunek klasteryzacji do długości ścieżek jest zaskakująco wysoki, co jest dystynktywną własnością sieci małego świata. Wreszcie, dokonujemy porównania cech omawianego modelu przepływu aktywności z neuro-biologicznymi rezultatami, przedstawionymi w badaniach grafów mózgowych z danych uzyskanych z funkcjonalnego obrazowania z wykorzystaniem rezonansu magnetycznego (fMRI). Wskazujemy silną odpowiedniość pomiędzy kształtem i wartością wykładnika rozkładu stopni, zaś klasteryzacja i charakterystyczna długość ścieżki są porównywalne w modelu i danych medycznych

Influencing robot learning through design and social interactions: a framework for balancing designer effort with active and explicit interactions

This thesis examines a balance between designer effort required in biasing a robot’s learn-ing of a task, and the effort required from an experienced agent in influencing the learning using social interactions, and the effect of this balance on learning performance. In order to characterise this balance, a two dimensional design space is identified, where the dimensions represent the effort from the designer, who abstracts the robot’s raw sensorimotor data accord-ing to the salient parts of the task to increasing degrees, and the effort from the experienced agent, who interacts with the learner robot using increasing degrees of complexities to actively accentuate the salient parts of the task and explicitly communicate about them. While the in-fluence from the designer must be imposed at design time, the influence from the experienced agent can be tailored during the social interactions because this agent is situated in the environ-ment while the robot is learning. The design space is proposed as a general characterisation of robotic systems that learn from social interactions. The usefulness of the design space is shown firstly by organising the related work into the space, secondly by providing empirical investigations of the effect of the various influences o