A Self-Organizing Neural Model for Motor Equivalent Phoneme Production

This paper describes a model of speech production called DIVA that highlights issues of self-organization and motor equivalent production of phonological units. The model uses a circular reaction strategy to learn two mappings between three levels of representation. Data on the plasticity of phonemic perceptual boundaries motivates a learned mapping between phoneme representations and vocal tract variables. A second mapping between vocal tract variables and articulator movements is also learned. To achieve the flexible control made possible by the redundancy of this mapping, desired directions in vocal tract configuration space are mapped into articulator velocity commands. Because each vocal tract direction cell learns to activate several articulator velocities during babbling, the model provides a natural account of the formation of coordinative structures. Model simulations show automatic compensation for unexpected constraints despite no previous experience or learning under these constraints.

A Memory Model for Concept Hierarchy Representation and Commonsense Reasoning

Most associative memory models perform one level mapping between predefined sets of input and output patterns1 and are unable to represent hierarchical knowledge. Complex AI systems allow hierarchical representation of concepts, but generally do not have learning capabilities. In this paper, a memory model is proposed which forms concept hierarchy by learning sample relations between concepts. All concepts are represented in a concept layer. Relations between a concept and its defining lower level concepts, are chunked as cognitive codes represented in a coding layer. By updating memory contents in the concept layer through code firing in the coding layer, the system is able to perform an important class of commonsense reasoning, namely recognition and inheritance.

ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation

A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3-D object recognition from a series of ambiguous 2-D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.

Self-Organizing Grammar Induction Using a Neural Network Model

This paper presents a self-organizing, real-time, hierarchical neural network model of sequential processing, and shows how it can be used to induce recognition codes corresponding to word categories and elementary grammatical structures. The model, first introduced in Mannes (1992), learns to recognize, store, and recall sequences of unitized patterns in a stable manner, either using short-term memory alone, or using long-term memory weights. Memory capacity is only limited by the number of nodes provided. Sequences are mapped to unitized patterns, making the model suitable for hierarchical operation. By using multiple modules arranged in a hierarchy and a simple mapping between output of lower levels and the input of higher levels, the induction of codes representing word category and simple phrase structures is an emergent property of the model. Simulation results are reported to illustrate this behavior.

A Neural Network Model of Speech Acquisition and Motor Equivalent Speech Production

This article describes a neural network model that addresses the acquisition of speaking skills by infants and subsequent motor equivalent production of speech sounds. The model learns two mappings during a babbling phase. A phonetic-to-orosensory mapping specifies a vocal tract target for each speech sound; these targets take the form of convex regions in orosensory coordinates defining the shape of the vocal tract. The babbling process wherein these convex region targets are formed explains how an infant can learn phoneme-specific and language-specific limits on acceptable variability of articulator movements. The model also learns an orosensory-to-articulatory mapping wherein cells coding desired movement directions in orosensory space learn articulator movements that achieve these orosensory movement directions. The resulting mapping provides a natural explanation for the formation of coordinative structures. This mapping also makes efficient use of redundancy in the articulator system, thereby providing the model with motor equivalent capabilities. Simulations verify the model's ability to compensate for constraints or perturbations applied to the articulators automatically and without new learning and to explain contextual variability seen in human speech production.

A Network for Learning Kinematics with Application to Human Reaching Models

A model for self-organization of the coordinate transformations required for spatial reaching is presented. During a motor babbling phase, a mapping from spatial coordinate directions to joint motion directions is learned. After learning, the model is able to produce straight-line spatial velocity trajectories with characteristic bell-shaped spatial velocity profiles, as observed in human reaches. Simulation results are presented for transverse plane reaching using a two degree-of-freedom arm.

3-D Object Recognition by the ART-EMAP Evidence Accumulation Network

ART-EMAP synthesizes adaptive resonance theory (AHT) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). The network extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage I introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Simulations of the four ART-EMAP stages demonstrate performance on a difficult 3-D object recognition problem.

Laminar Cortical Dynamics of Cognitive and Motor Working Memory, Sequence Learning and Performance: Toward a Unified Theory of How the Cerebral Cortex Works

How do the layered circuits of prefrontal and motor cortex carry out working memory storage, sequence learning, and voluntary sequential item selection and performance? A neural model called LIST PARSE is presented to explain and quantitatively simulate cognitive data about both immediate serial recall and free recall, including bowing of the serial position performance curves, error-type distributions, temporal limitations upon recall, and list length effects. The model also qualitatively explains cognitive effects related to attentional modulation, temporal grouping, variable presentation rates, phonemic similarity, presentation of non-words, word frequency/item familiarity and list strength, distracters and modality effects. In addition, the model quantitatively simulates neurophysiological data from the macaque prefrontal cortex obtained during sequential sensory-motor imitation and planned performance. The article further develops a theory concerning how the cerebral cortex works by showing how variations of the laminar circuits that have previously clarified how the visual cortex sees can also support cognitive processing of sequentially organized behaviors.

Multi-Area Visuotopic Map Complexes in Macaque Striate and Extra-striate Cortex

We propose that a simple, closed-form mathematical expression--the Wedge-Dipole mapping--provides a concise approximation to the full-field, two-dimensional topographic structure of macaque V1, V2, and V3. A single map function, which we term a map complex, acts as a simultaneous descriptor of all three areas. Quantitative estimation of the Wedge-Dipole parameters is provided via 2DG data of central-field V1 topography and a publicly available data set of full-field macaque V1 and V2 topography. Good quantitative agreement is obtained between the data and the model presented here. The increasing importance of fMRI-based brain imaging motivates the development of more sophisticated two-dimensional models of cortical visuotopy, in contrast to the one-dimensional approximations that have been in common use. One reason is that topography has traditionally supplied an important aspect of "ground truth", or validation, for brain imaging, suggesting that further development of high-resolution fMRI will be facilitated by this data analysis. In addition, several important insights into the nature of cortical topography follows from this work. The presence of anisotropy in cortical magnification factor is shown to follow mathematically from the shared boundary conditions at the V1-V2 and V2-V3 borders, and therefore may not causally follow from the existence of columnar systems in these areas, as is widely assumed. An application of the Wedge-Dipole model to localizing aspects of visual processing to specific cortical areas--extending previous work in correlating V1 cortical magnification factor to retinal anatomy or visual psychophysics data--is briefly discussed.

A Self-Organizing Neural Network Model for Redundant Sensory-Motor Control, Motor Equivalence, and Tool Use

A neural network is introduced which provides a solution of the classical motor equivalence problem, whereby many different joint configurations of a redundant manipulator can all be used to realize a desired trajectory in 3-D space. To do this, the network self-organizes a mapping from motion directions in 3-D space to velocity commands in joint space. Computer simulations demonstrate that, without any additional learning, the network can generate accurate movement commands that compensate for variable tool lengths, clamping of joints, distortions of visual input by a prism, and unexpected limb perturbations. Blind reaches have also been simulated.

Mapping the gene for autosomal dominant restless legs syndrome in an Irish family

Restless Legs Syndrome (RLS) is a common neurological disorder affecting nearly 15% of the general population. Ironically, RLS can be described as the most common condition one has never heard of. It is usually characterised by uncomfortable, unpleasant sensations in the lower limbs inducing an uncontrollable desire to move the legs. RLS exhibits a circadian pattern with symptoms present predominantly in the evening or at night, thus leading to sleep disruption and daytime somnolence. RLS is generally classified into primary (idiopathic) and secondary (symptomatic) forms. Primary RLS includes sporadic and familial cases of which the age of onset is usually less than 45 years and progresses slowly with a female to male ratio of 2:1. Secondary forms often occur as a complication of another health condition, such as iron deficiency or thyroid dysfunction. The age of onset is usually over 45 years, with an equal male to female ratio and more rapid progression. Ekbom described the familial component of the disorder in 1945 and since then many studies have been published on the familial forms of the disorder. Molecular genetic studies have so far identified ten loci (5q, 12q, 14p, 9p, 20p, 16p, 19p, 4q, 17p). No specific gene within these loci has been identified thus far. Association mapping has highlighted a further five areas of interest. RLS6 has been found to be associated with SNPs in the BTBD9 gene. Four other variants were found within intronic and intergenic regions of MEIS1, MAP2K5/LBXCOR1, PTPRD and NOS1. The pathophysiology of RLS is complex and remains to be fully elucidated. Conditions associated with secondary RLS, such as pregnancy or end-stage renal disease, are characterised by iron deficiency, which suggests that disturbed iron homeostasis plays a role. Dopaminergic dysfunction in subcortical systems also appears to play a central role. An ongoing study within the Department of Pathology (University College Cork) is investigating the genetic characteristics of RLS in Irish families. A three generation RLS pedigree RLS3002 consisting of 11 affected and 7 unaffected living family members was recruited. The family had been examined for four of the known loci (5q, 12q, 14p and 9p) (Abdulrahim 2008). The aim of this study was to continue examining this Irish RLS pedigree for possible linkage to the previously described loci and associated regions. Using informative microsatellite markers linkage was excluded to the loci on 5q, 12q, 14p, 9p, 20p, 16p, 19p, 4q, 17p and also within the regions reported to be associated with RLS. This suggested the presence of a new unidentified locus. A genome-wide scan was performed using two microsatellite marker screening sets (Research Genetics Inc. Mapping set and the Applied Biosystems Linkage mapping set version 2.5). Linkage analysis was conducted under an autosomal dominant model with a penetrance of 95% and an allele frequency of 0.01. A maximum LOD score of 3.59 at θ=0.00 for marker D19S878 indicated significant linkage on chromosome 19p. Haplotype analysis defined a genetic region of 6.57 cM on chromosome 19p13.3, corresponding to 2.5 Mb. There are approximately 100 genes annotated within the critical region. Sequencing of two candidate genes, KLF16 and GAMT, selected on the assumed pathophysiology of RLS, did not identify any sequence variant. This study provides evidence of a novel RLS locus in an Irish pedigree, thus supporting the picture of RLS as a genetically heterogeneous trait.

Assessing the impulse control of violent offenders using computer-based cognitive performance tasks

The topic of this thesis is impulsivity. The meaning and measurement of impulse control is explored, with a particular focus on forensic settings. Impulsivity is central to many areas of psychology; it is one of the most common diagnostic criteria of mental disorders and is fundamental to the understanding of forensic personalities. Despite this widespread importance there is little agreement as to the definition or structure of impulsivity, and its measurement is fraught with difficulty owing to a reliance on self-report methods. This research aims to address this problem by investigating the viability of using simple computerised cognitive performance tasks as complementary components of a multi-method assessment strategy for impulse control. Ultimately, the usefulness of this measurement strategy for a forensic sample is assessed. Impulsivity is found to be a multifaceted construct comprised of a constellation of distinct sub-dimensions. Computerised cognitive performance tasks are valid and reliable measures that can assess impulsivity at a neuronal level. Self-report and performance task methods assess distinct components of impulse control and, for the optimal assessment of impulse control, a multi-method battery of self-report and performance task measures is advocated. Such a battery is shown to have demonstrated utility in a forensic sample, and recommendations for forensic assessment in the Irish context are discussed.

An investigation on the use of SNR distributions for the optimisation of coarse-fine spectrum sensing for cognitive radio

This thesis investigates the optimisation of Coarse-Fine (CF) spectrum sensing architectures under a distribution of SNRs for Dynamic Spectrum Access (DSA). Three different detector architectures are investigated: the Coarse-Sorting Fine Detector (CSFD), the Coarse-Deciding Fine Detector (CDFD) and the Hybrid Coarse-Fine Detector (HCFD). To date, the majority of the work on coarse-fine spectrum sensing for cognitive radio has focused on a single value for the SNR. This approach overlooks the key advantage that CF sensing has to offer, namely that high powered signals can be easily detected without extra signal processing. By considering a range of SNR values, the detector can be optimised more effectively and greater performance gains realised. This work considers the optimisation of CF spectrum sensing schemes where the security and performance are treated separately. Instead of optimising system performance at a single, constant, low SNR value, the system instead is optimised for the average operating conditions. The security is still provided such that at the low SNR values the safety specifications are met. By decoupling the security and performance, the system’s average performance increases whilst maintaining the protection of licensed users from harmful interference. The different architectures considered in this thesis are investigated in theory, simulation and physical implementation to provide a complete overview of the performance of each system. This thesis provides a method for estimating SNR distributions which is quick, accurate and relatively low cost. The CSFD is modelled and the characteristic equations are found for the CDFD scheme. The HCFD is introduced and optimisation schemes for all three architectures are proposed. Finally, using the Implementing Radio In Software (IRIS) test-bed to confirm simulation results, CF spectrum sensing is shown to be significantly quicker than naive methods, whilst still meeting the required interference probability rates and not requiring substantial receiver complexity increases.

Towards a cognitive neurobiology of brain-gut axis disorders

The past two decades have seen substantial gains in our understanding of the complex processes underlying disturbed brain-gut communication in disorders such as irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD). Despite a growing understanding of the neurobiology of brain-gut axis dysfunction, there is a relative paucity of investigations into how the various factors involved in dysregulating the brain-gut axis, including stress, immune activation and pain, could impact on fundamental brain processes such as cognitive performance. To this end, we proposed a cognitive neurobiology of brain-gut axis dysfunction and took a novel approach to examine how disturbed brain-gut interactions may manifest as altered cognitive performance in IBS and IBD, both cross-sectionally and prospectively. We have demonstrated that, disorders of the brain-gut axis are characterised by stable deficits in specific cognitive domains. Specifically, patients with IBS exhibit a consistent hippocampal mediated visuospatial memory impairment. In addition we have found evidence to suggest a similar visuospatial impairment in IBD. However, our most consistent finding within this population was that patients with Crohn’s disease exhibit impaired selective attention/ response inhibition on the classic Stroop interference test. These cognitive deficits may serve to perpetuate and sustain brain-gut axis dysfunction. Furthermore, this research has shed light on some of the underlying neurobiological mechanisms that may be mediating cognitive dysfunction in IBS. Our findings may have significant implications for the individual who suffers from a brain-gut axis disorder and may also inform future treatment strategies. Taken together, these findings can be incorporated into existing neurobiological models of brain-gut axis dysfunction, to develop a more comprehensive model accounting for the cognitive-neurobiology of brain-gut axis disorders. This has furthered our understanding of disease pathophysiology and may ultimately aid in both the diagnosis and treatment of these highly prevalent, but poorly understood disorders.

Who pays and who plays? Mapping the discourse of publicly funded instrumental music education in Ireland

Instrumental music education is provided as an extra-curricular activity on a fee-paying basis by a small number of Education and Training Boards, formerly Vocational Education Committees (ETB/VECs) through specialist instrumental Music Services. Although all citizens’ taxes fund the public music provision, participation in instrumental music during school-going years is predominantly accessed by middle class families. A series of semistructured interviews sought to access the perceptions and beliefs of instrumental music education practitioners (N=14) in seven publicly-funded music services in Ireland. Canonical dispositions were interrogated and emergent themes were coded and analysed in a process of Grounded theory. The study draws on Foucault’s conception of discourse as a lens with which to map professional practices, and utilises Bourdieu’s analysis of the reproduction of social advantage to examine cultural assumptions, which may serve to privilege middle-class cultural choice to the exclusion of other social groups. Study findings show that within the Music Services, aesthetic and pedagogic discourses of the 19th century Conservatory system exert a hegemonic influence over policy and practice. An enduring ‘examination culture’ located within the Western art music tradition determines pedagogy, musical genre, and assessment procedures. Ideologies of musical taste and value reinforce the more tangible boundaries of fee-payment and restricted availability as barriers to access. Practitioners are aware of a status duality whereby instrumental teachers working as visiting specialists in primary schools experience a conflict between specialist and generalist educational aims. Nevertheless, study participants consistently advocated siting the point of access to instrumental music education in the primary schools as the most equitable means of access to instrumental music education. This study addresses a ‘knowledge gap’ in the sociology of music education in Ireland. It provides a framework for rethinking instrumental music education as equitable in-school musical participation. The conclusions of the study suggest starting-points for further educational research and may provide key ‘prompts’ for curriculum planning.