Koniocortex-like network unsupervised learning surpasses supervised results on WBCD breast cancer database

Koniocortex-Like Network is a novel category of Bio-Inspired Neural Networks whose architecture and properties are inspired in the biological koniocortex, the ?rst layer of the cortex that receives information from the thalamus. In the Koniocortex-Like Network competition and pattern classi?cation emerges naturally due to the interplay of inhibitory interneurons, metaplasticity and intrinsic plasticity. Recently proposed, it has shown a big potential for complex tasks with unsupervised learning. Now for the ?rst time, its competitive results are proved in a relevant standard real application that is the objective of state-ofthe-art research: the diagnosis of breast cancer data from the Wisconsin Breast Cancer Databas

Diseño de Redes neuronales artificiales no supervisadas orientadas a la inteligencia de negocio

El cerebro humano es el sistema de cálculo más complejo que conoce el hombre, de hecho existe el mito de que no usamos ni el 10% de su capacidad. Cada vez son más frecuentes los programas computacionales que se inspiran en el funcionamiento del cerebro para realizar automáticamente tareas que percibimos como inteligentes. Estos programas están inspirados en la capacidad del cerebro para pensar, recordar, reconocer y resolver problemas, y poder obtener información de una serie de datos para nuestro interés. Existen varios tipos de redes neuronales artificiales, que se pueden clasificar en dos grandes grupos: de aprendizaje supervisado y no supervisado. Entre las pertenecientes al segundo, se pueden destacar las redes de Teuvo Kohonen y la Koniocortex Like Network, las pruebas experimentales se han realizado sobre la primera red, introduciendo finalmente el uso de la segunda. La red de Kohonen está basada en mapas autorganizados descubiertos a nivel cerebral. Este tipo de red posee un aprendizaje no supervisado competitivo, es decir, que no existe un supervisor externo que dé como correcta o incorrecta la operación que haya realizado la red porque no se dispone de ninguna salida hacia la cual la red neuronal debe tender. El procedimiento que seguirá la red es, de manera auto-organizada, agrupar o mapear rasgos comunes, regularidades, categorías en los datos de entrada, etc., e incorporarlos a su estructura interna de conexiones. En este trabajo, a efectos ilustrativos, se ha comenzado a utilizar esta red para clasificar varias plantas de Iris, donde se le pasan 150 patrones y la red los organiza activando a tres neuronas diferentes. Una vez entendido el funcionamiento de la red de Kohonen, aplicaremos esta red para organizar una serie de datos bancarios y obtener información que pudiera ser aplicada para obtener mayor rendimiento en el negocio de la banca

Audition, learning and experience: expertise through development

Our experience with the auditory world can shape and modify perceptual, cognitive and neural processes with respect to audition. Such experience can occur over multiple timescales, and can vary in its specificity and intensity. In order to understand how auditory perceptual, cognitive and neural processes develop, it is important to explore the different means through which experience can influence audition. This thesis aims to address these issues. Using an expertise framework, we explore how the auditory environment and ontogenetic factors can shape and guide perceptual, cognitive and neural processes through long- and short-term profiles of experience. In early chapters, we use expertly-trained musicians as a model for long-term experience accrued under specific auditory constraints. We find that expertise on a particular instrument (violin versus piano) yields training-specific auditory perceptual advantages in a musical context, as well as improvements to ‘low-level’ auditory acuity (versus non-musicians); yet we find limited generalisation of expertise to cognitive tasks that require some of the skills that musicians hone. In a subsequent chapter, we find that expert violinists (versus non-musicians) show subtle increases in quantitative MR proxies for cortical myelin at left auditory core. In latter chapters, we explore short-term sound learning. We ask whether listeners can learn combinations of auditory cues within an active visuo-spatial task, and whether development can mediate learning of auditory cue combinations or costs due to cue contingency violations. We show that auditory cue combinations can be learned within periods of minutes. However, we find wide variation in cue learning success across all experiments, with no differences in overall cue combination learning between children and adults. These experiments help to further understanding of auditory expertise, learning, development and plasticity, within an experience-based framework

Cognitive Analysis of Complex Acoustic Scenes

Natural auditory scenes consist of a rich variety of temporally overlapping sounds that originate from multiple sources and locations and are characterized by distinct acoustic features. It is an important biological task to analyze such complex scenes and extract sounds of interest. The thesis addresses this question, also known as the “cocktail party problem” by developing an approach based on analysis of a novel stochastic signal contrary to deterministic narrowband signals used in previous work. This low-level signal, known as the Stochastic Figure-Ground (SFG) stimulus captures the spectrotemporal complexity of natural sound scenes and enables parametric control of stimulus features. In a series of experiments based on this stimulus, I have investigated specific behavioural and neural correlates of human auditory figure-ground segregation. This thesis is presented in seven sections. Chapter 1 reviews key aspects of auditory processing and existing models of auditory segregation. Chapter 2 presents the principles of the techniques used including psychophysics, modeling, functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). Experimental work is presented in the following chapters and covers figure-ground segregation behaviour (Chapter 3), modeling of the SFG stimulus based on a temporal coherence model of auditory perceptual organization (Chapter 4), analysis of brain activity related to detection of salient targets in the SFG stimulus using fMRI (Chapter 5), and MEG respectively (Chapter 6). Finally, Chapter 7 concludes with a general discussion of the results and future directions for research. Overall, this body of work emphasizes the use of stochastic signals for auditory scene analysis and demonstrates an automatic, highly robust segregation mechanism in the auditory system that is sensitive to temporal correlations across frequency channels

Audition, learning and experience: expertise through development

Our experience with the auditory world can shape and modify perceptual, cognitive and neural processes with respect to audition. Such experience can occur over multiple timescales, and can vary in its specificity and intensity. In order to understand how auditory perceptual, cognitive and neural processes develop, it is important to explore the different means through which experience can influence audition. This thesis aims to address these issues. Using an expertise framework, we explore how the auditory environment and ontogenetic factors can shape and guide perceptual, cognitive and neural processes through long- and short-term profiles of experience. In early chapters, we use expertly-trained musicians as a model for long-term experience accrued under specific auditory constraints. We find that expertise on a particular instrument (violin versus piano) yields training-specific auditory perceptual advantages in a musical context, as well as improvements to ‘low-level’ auditory acuity (versus non-musicians); yet we find limited generalisation of expertise to cognitive tasks that require some of the skills that musicians hone. In a subsequent chapter, we find that expert violinists (versus non-musicians) show subtle increases in quantitative MR proxies for cortical myelin at left auditory core. In latter chapters, we explore short-term sound learning. We ask whether listeners can learn combinations of auditory cues within an active visuo-spatial task, and whether development can mediate learning of auditory cue combinations or costs due to cue contingency violations. We show that auditory cue combinations can be learned within periods of minutes. However, we find wide variation in cue learning success across all experiments, with no differences in overall cue combination learning between children and adults. These experiments help to further understanding of auditory expertise, learning, development and plasticity, within an experience-based framework

Design and use of novel non-invasive head immobilisation method for investigation of behavioural and functional asymmetries in non-human primate auditory cortex

PhD ThesisThis project was initiated with two goals in mind. The first, to refine methods of head immobilisation for rhesus macaques participating in experiments which do not require direct access to the brain, and the second to investigate the effect of attention on lateralisation in auditory cortex. Head immobilisation is often necessary for neuroscientific procedures. A number of Non-invasive Head Immobilisation Systems (NHIS) for monkeys are available, but the need remains for a feasible integrated system combining a broad range of essential features. This thesis details the development of an individually customised macaque NHIS which addresses several animal welfare and scientific needs. The system comprises a customised facemask that can be used separately or combined with a back piece to form a full head helmet. The system was evaluated during performance on several auditory or visual behavioural tasks with testing sessions lasting 1.5-2hrs. To investigate the effect of attention on lateralised processes, four male rhesus macaques were trained to perform an active auditory spatial discrimination task (two of which used the NHIS) using either conspecific “coo” vocalisations or a coo vocalisation from a different individual which had the phase information scrambled, but preserved the spectral components (sCoo). Behavioural results indicated a directional bias during the task with coos, with the animals performing the task with ease when the coo initially appeared on the left but performance being hindered when the coo first appeared on the right. No bias was observed with an animal initially trained with the noise. Attention effects on hemispheric laterality were then studied using fMRI with the trained animals and, as a point of reference, a naïve animal who was passively presented with the task stimuli. The results shown have implications for the control of attention when investigating lateralised processing in both human and non-human species. Additionally, it is conclusively shown that auditory fMRI and behavioural experiments can be conducted without the need for invasive head immobilisation techniques in rhesus macaques

CHOLINERGIC CONTRIBUTIONS TO EMOTION REGULATION

Theories based on clinical and neuroanatomical studies implicate the muscarinic cholinergic system in normal and pathological emotion regulation. Emotional and sensory experiences can be induced with intravenous administration of the local anesthetic procaine hydrochloride, which selectively activates limbic regions in humans and animals. Procaine has a high affinity for muscarinic cholinergic receptors in vitro. This research tests three hypotheses: (1) procaine binds to muscarinic receptors in vivo; (2) procaine alters functional connectivity among cholinergic brain regions and their targets; and (3) procaine-induced emotions are related to core cholinergic regions. In Experiment I, anesthetized rhesus monkeys underwent positron emission tomography (PET) studies before and after administration of six doses of procaine on separate days using a radioligand with preferential binding to muscarinic M2 receptors ([18F]FP-TZTP). Procaine blocked [18F]FP-TZTP in a dose-response fashion uniformly across the brain, while significantly increasing tracer flow in limbic compared with non-limbic regions. In Experiment II, behavioral and physiological measures were assessed at baseline and following procaine in 32 healthy controls and 15 patients with bipolar disorder undergoing [15O] PET yielding regional cerebral blood flow (rCBF). Procaine selectively increased rCBF in anterior paralimbic regions in healthy controls, but to a lesser degree in patients. Regions connected via cholinergic pathways showed significantly different functional connectivity in both groups with procaine, however, prefrontal regions showed differential functional connectivity with cholinergic brain regions in patients compared with controls. Changes in activity of cholinergic regions explained the variance in anxiety ratings in an opposite manner in each group, and in euphoria ratings only in patients. In conclusion, procaine binds directly with muscarinic receptors in vivo while selectively increasing limbic activity in anesthetized monkeys. Two key findings herein procaine-induced alterations in functional connectivity of core cholinergic regions in humans, and the association of core cholinergic regional activity with emotional experience support theories implicating cholinergic contributions to emotion regulation. Decreased anterior paralimbic activity and altered functional connectivity of cholinergic regions in patients with bipolar illness compared with controls revealed by procaine offers additional insight into the regional neurobiology of the disease, and may ultimately be targeted in therapeutic approaches to bipolar disorder

The effects of distraction, relaxation, and guided imagery on procedural fear and pain in children

The fear and pain of medical procedures are a source of great distress to children. Techniques such as distraction, relaxation and guided imagery help children to cope, and in some cases, have a marked influence on the experience of fear and pain during painful medical procedures. However, the effects, embedded in the relationships between consciousness, imagery, fear and pain, are unclear, particularly with regard to the clinical (as opposed to the laboratory) reality of procedural pain. The aim of this thesis was to empirically account for the therapeutic effects of distraction, relaxation, and imagery on procedural fear and pain in children and to offer a model based on a constructive view of experience allied to recent advances in neurophysiology that could account for the effects. Two studies were undertaken to address this aim. The first study investigated the effects of cartoon distraction on fear and pain in children undergoing venepuncture. The second study investigated the independent and combined effects of relaxation and imagery on fear and pain in children also undergoing venepuncture. The studies indicated that relaxation, distraction and imagery reduced procedural fear. Procedural pain was not affected by relaxation but distraction showed positive effects as did imagery, particularly if procedural pain was defined in terms of its sensory and emotional components. These effects are explained using a model based on a top-down constructivist view of the psychology and neurophysiology of fear, pain, imagery and consciousness. The neurophysiological components of the model comprised the amygdala, anterior cingulate cortex and association areas within a working memory view of consciousness. The constructivist perspective held that during relaxation the child’s cognitive, emotional and sensorial quality were largely based on the ‘reality’ of the procedure room, but that during imagery and perhaps distraction, the qualia were located elsewhere. The thesis concludes with the relevance of the model for clinical practice and implications for further psychological and neurophysiological research.Doctor of Philosoph

Contribution of artificial metaplasticity to pattern recognition

Artificial Neural Networks design and training algorithms are based many times on the optimization of an objective error function used to provide an evaluation of the performances of the network. The value of the error depends basically on the weight values of the different connections between the neurons of the network. The learning methods modify and update the different weight values following a strategy that tends to minimize the final error in the network performance. The neural network theory identifies the weight values as a representation of the synaptic weights in the biological neural networks, and their ability to change their values can be interpreted as a kind of artificial plasticity inspired by the demonstrated biological counterpart process. The biological metaplasticity is related to the processes of memory and learning as an inherent property of the biological neuron connections, and consists in the capacity of modifying the learning mechanism using the information present in the network itself. In such a way, Artificial MetaPlasticity (AMP), is interpreted as the ability to change the efficiency of artificial plasticity depending on certain elements used in the training. A very efficient AMP model (as a function of learning time and performance) is the approach that connects metaplasticity and Shannon’s information theory, which establishes that less frequent patterns carry more information than frequent patterns. This model defines AMP as a learning procedure that produces greater modifications in the synaptic weights when less frequent patterns are presented to the network than when frequent patterns are used, as a way of extracting more information from the former than from the latter. In this doctoral thesis the AMP theory is implemented using different Artificial Neural Network (ANN), models and different learning paradigms. The networks are used as classifiers or predictors of synthetic and real data sets in order to be able to compare and evaluate the results obtained with several state of the art methods. The AMP theory is implemented over two general learning methods: • Supervised training: The BackPropagation Algorithm (BPA), is one of the best known and most used algorithms to training the neural networks. This algorithm compares the ideal results with the real results obtained at the networks output and calculates an error value. This value is used to modify the weight values in order to get a final trained network that minimizes the differences between the ideal and the real results. The BPA has been successfully applied to several patter classification problems in areas such as: medicine, bioinformatic, banking, climatological predictions, etc. However the classic algorithm has shown some limitations that prevent this method to reach an optimal efficiency level (convergence, speed problems and classification accuracy). Artificial Metaplasticity modification to the classic BPA, is in this case implemented in a Multilayer Perceptron (MLP), neural network. The Artificial Metaplasticity on MultiLayer Perceptron (AMMLP) model was applied in the ANNs training phase. During the training phase the AMMLP algorithm updates the weights assigning higher values to the less frequent activations than to the more frequent ones. AMMLP achieves a more efficient training and improves MLP performance. The suggested AMMLP algorithm was applied to different problems related to pattern classification or prediction in different areas and considering different methods for obtaining the information from the data set. Modeling this interpretation in the training phase, the hypothesis of an improved training shows a much more efficient training maintaining the ANN performance. This algorithm has achieved deeper learning on several multidisciplinary data sets without the need of a deep network. • Unsupervised training: Koniocortex-Like Network (KLN) is a novel category of bio-inspired neural networks whose architecture and properties are inspired in the biological koniocortex, the first layer of the cortex that receives information from the thalamus. In the KLN competition and pattern classification emerges naturally due to the interplay of inhibitory inter-neurons, metaplasticity and intrinsic plasticity. This behavior resembles a Winner Take All (WTA) mode of operation, where the most active neuron “wins”, i.e. fires, while neighboring ones remain silent. Although a winning neuron is identified by calculation in many artificial neural networks models, in biological neural networks the winning neuron emerges from a natural dynamic process. Recently proposed, it has shown a big potential for complex tasks with unsupervised learning. Now for the first time, its competitive results are proved in several relevant real applications. The simulations show that the unsupervised learning that emerges from individual neurons properties is comparable and even surpasses results obtained with several advanced state-of-the-art supervised and unsupervised learning algorithms