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Evanshen, Pamela, and L. Phillips. "Brain Compatible Learning Environments." Digital Commons @ East Tennessee State University, 2005. https://dc.etsu.edu/etsu-works/4368.
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Evanshen, Pamela. "Brain-compatible Learning Environments." Digital Commons @ East Tennessee State University, 2007. https://dc.etsu.edu/etsu-works/4404.
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Thurston, Roy J. "Brain injury, memory and learning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0024/NQ49543.pdf.
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Brodnax, Rita M. "Brain compatible teaching for learning." [Bloomington, Ind.] : Indiana University, 2004. http://wwwlib.umi.com/dissertations/fullcit/3173526.
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Thesis (Ed. D.)--Indiana University, Dept. of Educational Leadership, 2004.Title from PDF t.p. (viewed Dec. 8, 2008). Source: Dissertation Abstracts International, Volume: 66-04, Section: A, page: 1257. Chair: Ron Barnes.
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Parsapoor, Mahboobeh. "Brain Emotional Learning-Inspired Models." Licentiate thesis, Högskolan i Halmstad, Centrum för forskning om inbyggda system (CERES), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-25428.
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In this thesis the mammalian nervous system and mammalian brain have been used as inspiration to develop a computational intelligence model based on the neural structure of fear conditioning and to extend the structure of the previous proposed amygdala-orbitofrontal model. The proposed model can be seen as a framework for developing general computational intelligence based on the emotional system instead of traditional models on the rational system of the human brain. The suggested model can be considered a new data driven model and is referred to as the brain emotional learning-inspired model (BELIM). Structurally, a BELIM consists of four main parts to mimic those parts of the brain’s emotional system that are responsible for activating the fear response. In this thesis the model is initially investigated for prediction and classification. The performance has been evaluated using various benchmark data sets from prediction applications, e.g. sunspot numbers from solar activity prediction, auroral electroject (AE) index from geomagnetic storms prediction and Henon map, Lorenz time series. In most of these cases, the model was tested for both long-term and short-term prediction. The performance of BELIM has also been evaluated for classification, by classifying binary and multiclass benchmark data sets.
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Nair, Hemanth P. "Brain imaging of developmental learning effects /." Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p3004348.
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Sperlich, Juntana Ginda. "Designing a brain-based learning environment." CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3216.
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The purpose of this project was to develop a teacher friendly guide that would help teachers not only apply brain-based strategies in the classroom, but also to see results from transforming their classrooms into brain-based learning environments.
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Oscarsson, Jacob. "Exploring the Brain : Interactivity and Learning." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12329.
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This study has examined whether the use of an interactive 3D model of the human brain would be a more effective way of teaching it's anatomy in comparison to traditional book and paper-based techniques. The artefact created for the project was a three dimensional model of the brain made up of several anatomical structures that could be dissected to provide the user with a more accurate sense of the spatial relationships between each structure. The study conducted did not give sufficient information to accurately answer the research question, but interviews conducted during the experiment show interest in the technology. If developed, there could be potential for the use of this type of technology in the future.
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Amerineni, Rajesh. "BRAIN-INSPIRED MACHINE LEARNING CLASSIFICATION MODELS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1806.
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This dissertation focuses on the development of three classes of brain-inspired machine learning classification models. The models attempt to emulate (a) multi-sensory integration, (b) context-integration, and (c) visual information processing in the brain.The multi-sensory integration models are aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli. Two multimodal classification models are introduced: the feature integrating (FI) model and the decision integrating (DI) model. The FI model, inspired by multisensory integration in the subcortical superior colliculus, combines unimodal features which are subsequently classified by a multimodal classifier. The DI model, inspired by integration in primary cortical areas, classifies unimodal stimuli independently using unimodal classifiers and classifies the combined decisions using a multimodal classifier. The multimodal classifier models are be implemented using multilayer perceptrons and multivariate statistical classifiers. Experiments involving the classification of noisy and attenuated auditory and visual representations of ten digits are designed to demonstrate the properties of the multimodal classifiers and to compare the performances of multimodal and unimodal classifiers. The experimental results show that the multimodal classification systems exhibit an important aspect of the “inverse effectiveness principle” by yielding significantly higher classification accuracies when compared with those of the unimodal classifiers. Furthermore, the flexibility offered by the generalized models enables the simulations and evaluations of various combinations of multimodal stimuli and classifiers under varying uncertainty conditions. The context-integrating model emulates the brain’s ability to use contextual information to uniquely resolve the interpretation of ambiguous stimuli. A deep learning neural network classification model that emulates this ability by integrating weighted bidirectional context into the classification process is introduced. The model, referred to as the CINET, is implemented using a convolution neural network (CNN), which is shown to be ideal for combining target and context stimuli and for extracting coupled target-context features. The CINET parameters can be manipulated to simulate congruent and incongruent context environments and to manipulate target-context stimuli relationships. The formulation of the CINET is quite general; consequently, it is not restricted to stimuli in any particular sensory modality nor to the dimensionality of the stimuli. A broad range of experiments are designed to demonstrate the effectiveness of the CINET in resolving ambiguous visual stimuli and in improving the classification of non-ambiguous visual stimuli in various contextual environments. The fact that the performance improves through the inclusion of context can be exploited to design robust brain-inspired machine learning algorithms. It is interesting to note that the CINET is a classification model that is inspired by a combination of brain’s ability to integrate contextual information and the CNN, which is inspired by the hierarchical processing of visual information in the visual cortex. A convolution neural network (CNN) model, inspired by the hierarchical processing of visual information in the brain, is introduced to fuse information from an ensemble of multi-axial sensors in order to classify strikes such as boxing punches and taekwondo kicks in combat sports. Although CNNs are not an obvious choice for non-array data nor for signals with non-linear variations, it will be shown that CNN models can effectively classify multi-axial multi-sensor signals. Experiments involving the classification of three-axis accelerometer and three-axes gyroscope signals measuring boxing punches and taekwondo kicks showed that the performance of the fusion classifiers were significantly superior to the uni-axial classifiers. Interestingly, the classification accuracies of the CNN fusion classifiers were significantly higher than those of the DTW fusion classifiers. Through training with representative signals and the local feature extraction property, the CNNs tend to be invariant to the latency shifts and non-linear variations. Moreover, by increasing the number of network layers and the training set, the CNN classifiers offer the potential for even better performance as well as the ability to handle a larger number of classes. Finally, due to the generalized formulations, the classifier models can be easily adapted to classify multi-dimensional signals of multiple sensors in various other applications.
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Olsson, Joakim. "A Critique of the Learning Brain." Thesis, Uppsala universitet, Avdelningen för teoretisk filosofi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-432105.
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The guiding question for this essay is: who is the learner? The aim is to examine and criticize one answer to this question, sometimes referred to as the theory of the learning brain, which suggests that the explanation of human learning can be reduced to the transmitting and storing of information in the brain’s formal and representational architecture, i.e., that the brain is the learner. This essay will argue that this answer is misleading, because it cannot account for the way people strive to learn in an attempt to lead a good life as it misrepresents the intentional life of the mind, which results in its counting ourselves out of the picture when it attempts to provide a scientific theory of the learning process. To criticize the theory of the learning brain, this essay will investigate its philosophical foundation, a theory of mind called cognitivism, which is the basis for the cognitive sciences. Cognitivism is itself built on three main tenets: mentalism, the mind-brain identity theory and the computer analogy. Each of these tenets will be criticized in turn, before the essay turns to criticize the theory of the learning brain itself. The focus of this essay is, in other words, mainly negative. The hope is that this criticism will lay the groundwork for an alternative view of mind, one that is better equipped to give meaningful answers to the important questions we have about what it means to learn, i.e., what we learn, how we do it and why. This alternative will emphasize the holistic and intentional character of the human mind, and consider the learning process as an intentional activity performed, not by isolated brains, but by people with minds that are extended, embodied, enacted and embedded in a sociocultural and physical context.
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Morra, Jonathan Harold. "Learning methods for brain MRI segmentation." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1905693471&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
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Babalola, Karolyn Olatubosun. "Brain-computer interfaces for inducing brain plasticity and motor learning: implications for brain-injury rehabilitation." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41164.
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The goal of this investigation was to explore the efficacy of implementing a rehabilitation robot controlled by a noninvasive brain-computer interface (BCI) to influence brain plasticity and facilitate motor learning. The motivation of this project stemmed from the need to address the population of stroke survivors who have few or no options for therapy.
A stroke occurs every 40 seconds in the United States and it is the leading cause of long-term disability [1-3]. In a country where the elderly population is growing at an astounding rate, one in six persons above the age of 55 is at risk of having a stroke. Internationally, the rates of strokes and stroke-induced disabilities are comparable to those of the United States [1, 4-6]. Approximately half of all stroke survivors suffer from immediate unilateral paralysis or weakness, 30-60% of which never regain function [1, 6-9]. Many individuals who survive stroke will be forced to seek institutional care or long-term assistance.
Clinicians have typically implemented stroke rehabilitative treatment using active training techniques such as constraint induced movement therapy (CIMT) and robotic therapy [10-12]. Such techniques restore motor activity by forcing the movement of weakened limbs. That active engagement of the weakened limb movement stimulates neural pathways and activates the motor cortex, thus inducing brain plasticity and motor learning. Several studies have demonstrated that active training does in fact have an effect on the way the brain restores itself and leads to faster rehabilitation [10, 13-15]. In addition, studies involving mental practice, another form of rehabilitation, have shown that mental imagery directly stimulates the brain, but is not effective unless implemented as a supplemental to active training [16, 17]. Only stroke survivors retaining residual motor ability are able to undergo active rehabilitative training; the current selection of therapies has overlooked the significant population of stroke survivors suffering from severe control loss or complete paralysis [6, 10].
A BCI is a system or device that detects minute changes in brain signals to facilitate communication or control. In this investigation, the BCI was implemented through an electroencephalograph (EEG) device. EEG devices detect electrical brain signals transmitted through the scalp that corresponded with imagined motor activity. Within the BCI, a linear transformation algorithm converted EEG spectral features into control commands for an upper-limb rehabilitative robot, thus implementing a closed-looped feedback-control training system. The concept of the BCI-robot system implemented in this investigation may provide an alternative to current therapies by demonstrating the results of bypassing motor activity using brain signals to facilitate robotic therapy.
In this study, 24 able-bodied volunteers were divided into two study groups; one group trained to use sensorimotor rhythms (SMRs) (produced by imagining motor activity) to control the movement of a robot and the other group performed the 'guided-imagery' task of watching the robot move without control. This investigation looked for contrasts between the two groups that showed that the training involved with controlling the BCI-robot system had an effect on brain plasticity and motor learning.
To analyze brain plasticity and motor learning, EEG data corresponding to imagined arm movement and motor learning were acquired before, during, and after training. Features extracted from the EEG data consisted of frequencies in the 5-35Hz range, which produced amplitude fluctuations that were measurably significant during reaching. Motor learning data consisted of arm displacement measures (error) produced during an motor adaptation task performed daily by all subjects.
The results of the brain plasticity analysis showed persistent reductions in beta activity for subjects in the BCI group. The analysis also showed that subjects in the Non-BCI group had significant reductions in mu activity; however, these results were likely due to the fact that different EEG caps were used in each stage of the study. These results were promising but require further investigation.
The motor learning data showed that the BCI group out-performed non-BCI group in all measures of motor learning. These findings were significant because this was the first time a BCI had been applied to a motor learning protocol and the findings suggested that BCI had an influence on the speed at which subjects adapted to a motor learning task. Additional findings suggested that BCI subjects who were in the 40 and over age group had greater decreases in error after the learning phase of motor assessment. These finding suggests that BCI could have positive long term effects on individuals who are more likely to suffer from a stroke and possibly could be beneficial for chronic stroke patients.
In addition to exploring the effects of BCI training on brain plasticity and motor learning this investigation sought to detect whether the EEG features produced during guided-imagery could differentiate between reaching direction. While the analysis presented in this project produced classification accuracies no greater than ~77%, it formed the basis of future studies that would incorporate different pattern recognition techniques.
The results of this study show the potential for developing new rehabilitation therapies and motor learning protocols that incorporate BCI.
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Turaga, Srinivas C. "Learning image segmentation and hierarchies by learning ultrametric distances." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54626.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 100-105).
In this thesis I present new contributions to the fields of neuroscience and computer science. The neuroscientific contribution is a new technique for automatically reconstructing complete neural networks from densely stained 3d electron micrographs of brain tissue. The computer science contribution is a new machine learning method for image segmentation and the development of a new theory for supervised hierarchy learning based on ultrametric distance functions. It is well-known that the connectivity of neural networks in the brain can have a dramatic influence on their computational function . However, our understanding of the complete connectivity of neural circuits has been quite impoverished due to our inability to image all the connections between all the neurons in biological network. Connectomics is an emerging field in neuroscience that aims to revolutionize our understanding of the function of neural circuits by imaging and reconstructing entire neural circuits. In this thesis, I present an automated method for reconstructing neural circuitry from 3d electron micrographs of brain tissue. The cortical column, a basic unit of cortical microcircuitry, will produce a single 3d electron micrograph measuring many 100s terabytes once imaged and contain neurites from well over 100,000 different neurons. It is estimated that tracing the neurites in such a volume by hand would take several thousand human years. Automated circuit tracing methods are thus crucial to the success of connectomics. In computer vision, the circuit reconstruction problem of tracing neurites is known as image segmentation. Segmentation is a grouping problem where image pixels belonging to the same neurite are clustered together. While many algorithms for image segmentation exist, few have parameters that can be optimized using groundtruth data to extract maximum performance on a specialized dataset. In this thesis, I present the first machine learning method to directly minimize an image segmentation error. It is based the theory of ultrametric distances and hierarchical clustering. Image segmentation is posed as the problem of learning and classifying ultrametric distances between image pixels. Ultrametric distances on point set have the special property that
(cont.) they correspond exactly to hierarchical clustering of the set. This special property implies hierarchical clustering can be learned by directly learning ultrametric distances. In this thesis, I develop convolutional networks as a machine learning architecture for image processing. I use this powerful pattern recognition architecture with many tens of thousands of free parameters for predicting affinity graphs and detecting object boundaries in images. When trained using ultrametric learning, the convolutional network based algorithm yields an extremely efficient linear-time segmentation algorithm. In this thesis, I develop methods for assessing the quality of image segmentations produced by manual human efforts or by automated computer algorithms. These methods are crucial for comparing the performance of different segmentation methods and is used through out the thesis to demonstrate the quality of the reconstructions generated by the methods in this thesis.
by Srinivas C. Turaga.
Ph.D.
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Herson, Laurie A. "Brain-compatible research: using brain-based techniques to positively impact student learning." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001668.
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Bradford-Meyer, Connie. "Follow-up of brain conference attendees and their application of brain research : a questionnaire approach /." ProQuest subscription required:, 2003. http://proquest.umi.com/pqdweb?did=990270771&sid=1&Fmt=2&clientId=8813&RQT=309&VName=PQD.
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Havaei, Seyed Mohammad. "Machine learning methods for brain tumor segmentation." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10260.
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Abstract : Malignant brain tumors are the second leading cause of cancer related deaths in children under 20. There are nearly 700,000 people in the U.S. living with a brain tumor and 17,000 people are likely to loose their lives due to primary malignant and central nervous system brain tumor every year.
To identify whether a patient is diagnosed with brain tumor in a non-invasive way, an MRI scan of the brain is acquired followed by a manual examination of the scan by an expert who looks for lesions (i.e. cluster of cells which deviate from healthy tissue). For treatment purposes, the tumor and its sub-regions are outlined in a procedure known as brain tumor segmentation . Although brain tumor segmentation is primarily done manually, it is very time consuming and the segmentation is subject to variations both between observers and within the same observer. To address these issues, a number of automatic and semi-automatic methods have been proposed over the years to help physicians in the decision making process.
Methods based on machine learning have been subjects of great interest in brain tumor segmentation. With the advent of deep learning methods and their success in many computer vision applications such as image classification, these methods have also started to gain popularity in medical image analysis.
In this thesis, we explore different machine learning and deep learning methods applied to brain tumor segmentation.Résumé: Les tumeurs malignes au cerveau sont la deuxième cause principale de décès chez les enfants de moins de 20 ans. Il y a près de 700 000 personnes aux États-Unis vivant avec une tumeur au cerveau, et 17 000 personnes sont chaque année à risque de perdre leur vie suite à une tumeur maligne primaire dans le système nerveu central. Pour identifier de façon non-invasive si un patient est atteint d'une tumeur au cerveau, une image IRM du cerveau est acquise et analysée à la main par un expert pour trouver des lésions (c.-à-d. un groupement de cellules qui diffère du tissu sain). Une tumeur et ses régions doivent être détectées à l'aide d'une segmentation pour aider son traitement. La segmentation de tumeur cérébrale et principalement faite à la main, c'est une procédure qui demande beaucoup de temps et les variations intra et inter expert pour un même cas varient beaucoup. Pour répondre à ces problèmes, il existe beaucoup de méthodes automatique et semi-automatique qui ont été proposés ces dernières années pour aider les praticiens à prendre des décisions. Les méthodes basées sur l'apprentissage automatique ont suscité un fort intérêt dans le domaine de la segmentation des tumeurs cérébrales. L'avènement des méthodes de Deep Learning et leurs succès dans maintes applications tels que la classification d'images a contribué à mettre de l'avant le Deep Learning dans l'analyse d'images médicales. Dans cette thèse, nous explorons diverses méthodes d'apprentissage automatique et de Deep Learning appliquées à la segmentation des tumeurs cérébrales.
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Petersson, Karl Magnus. "Learning and memory in the human brain /." Stockholm, 2005. http://diss.kib.ki.se/2005/91-7140-304-3/.
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Munro, M., and M. Coetzee. "Mind the Gap: Beyond Whole-brain learning." South African Theatre Journal, 2008. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000808.
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In past research we have demonstrated how methodologies used in the training of performers can both encourage whole-brain learning and answer to the demands of South Africa’s current educational paradigm, outcomes-based education (OBE). OBE is a needs-driven, outcomes-driven and competency-orientated pedagogy, which aims at incorporating learners as active agents within the learning process as opposed to the previous content-driven, teacher-orientated approach to education (Coetzee 2004).
Our research was prompted by the constant need for our Drama departments to validate their existence in the light of changing funding structures for the arts, governmental and institutional demands for measured outcomes and our institutions’ emphasis on whole-brain learning as the preferred pedagogical approach to education and training. We explored the ways in which the changes in the South African educational dispensation impact on the work of educators within a Drama department in the Higher Education and Training band (HET) in South Africa. These changes include a focus on competencies and critical outcomes across learning areas and across the qualification bands identified by the new National Qualifications Framework. In our search for ways in which to implement the critical outcomes2 demanded by the OBE framework, we turned to Herrmann’s argument (1995) that optimal, deep structure learning can only take place when whole-brain modes are operative.
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Von, Aulock Maryna. "Brain compatible learning in the radiation sciences." Thesis, Peninsula Technikon, 2003. http://hdl.handle.net/20.500.11838/1549.
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Thesis (MTech (Radiography))--Peninsula Technikon, Cape Town, 2003Brain Compatible Learning (BCL), as its name suggests, is a type of learning which is aligned with how the human brain naturally learns and develops. BCL offers many different options and routes to learning as alternatives to conventional 'chalk and talk' methodologies. A BCL curriculum is planned to define the structure and content of a programme of learning, but it also provides opportunities for students to participate in activities, which encourage and enhance the development of an active and deep approach to learning. Using BCL approaches in the classroom thus creates both a stimulating and a caring environment for student learning. This project researches a BCL intervention in a Radiation Science course. The use of BCL techniques has tended to have been done predominantly in the social sciences; this research fills an important 'gap' in the research literature by examining how BCL might be implemented in a technical and scientific context. The research was conducted using an adapted Participatory Active Research methodology in which classroom interventions were planned (within a constructive framework), rather than implemented and then reflected on by all participants. The PAR method was supplemented with a series of detailed questionnaires and interviews. The broad findings of this study relate to students' experiences of BCL in Radiation Science in terms of 'process' and 'product" issues. In terms of process, or the methodology of BCL, students' responses were largely positive.
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Raina, Kevin. "Machine Learning Methods for Brain Lesion Delineation." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41156.
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Brain lesions are regions of abnormal or damaged tissue in the brain, commonly due
to stroke, cancer or other disease. They are diagnosed primarily using neuroimaging,
the most common modalities being Magnetic Resonance Imaging (MRI) or Computed
Tomography (CT). Brain lesions have a high degree of variability in terms of location,
size, intensity and form, which makes diagnosis challenging. Traditionally, radiologists
diagnose lesions by inspecting neuroimages directly by eye; however, this is time-consuming and subjective. For these reasons, many automated methods have been
developed for lesion delineation (segmentation), lesion identification and diagnosis.
The goal of this thesis is to improve and develop automated methods for delineating
brain lesions from multimodal MRI scans. First, we propose an improvement to existing segmentation methods by exploiting the bilateral quasi-symmetry of healthy
brains, which breaks down when lesions are present. We augment our data using
nonlinear registration of a neuroimage to a reflected version of itself, leading to an
improvement in Dice coefficient of 13 percent. Second, we model lesion volume in
brain image patches with a modified Poisson regression method. The model accurately identified the lesion image with the larger lesion volume for 86 percent of paired sample patches. Both of these projects were published in the proceedings of the BIOSTEC 2020 conference. In the last two chapters, we propose a confidence-based approach to measure segmentation uncertainty, and apply an unsupervised segmentation method based on mutual information.
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Zarogianni, Eleni. "Machine learning and brain imaging in psychosis." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22814.
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Over the past years early detection and intervention in schizophrenia have become a major objective in psychiatry. Early intervention strategies are intended to identify and treat psychosis prior to fulfilling diagnostic criteria for the disorder. To this aim, reliable early diagnostic biomarkers are needed in order to identify a high-risk state for psychosis and also predict transition to frank psychosis in those high-risk individuals destined to develop the disorder. Recently, machine learning methods have been successfully applied in the diagnostic classification of schizophrenia and in predicting transition to psychosis at an individual level based on magnetic resonance imaging (MRI) data and also neurocognitive variables. This work investigates the application of machine learning methods for the early identification of schizophrenia in subjects at high risk for developing the disorder. The dataset used in this work involves data from the Edinburgh High Risk Study (EHRS), which examined individuals at a heightened risk for developing schizophrenia for familial reasons, and the FePsy (Fruherkennung von Psychosen) study that was conducted in Basel and involves subjects at a clinical high-risk state for psychosis. The overriding aim of this thesis was to use machine learning, and specifically Support Vector Machine (SVM), in order to identify predictors of transition to psychosis in high-risk individuals, using baseline structural MRI data. There are three aims pertaining to this main one. (i) Firstly, our aim was to examine the feasibility of distinguishing at baseline those individuals who later developed schizophrenia from those who did not, yet had psychotic symptoms using SVM and baseline data from the EHRS study. (ii) Secondly, we intended to examine if our classification approach could generalize to clinical high-risk cohorts, using neuroanatomical data from the FePsy study. (iii) In a more exploratory context, we have also examined the diagnostic performance of our classifier by pooling the two datasets together. With regards to the first aim, our findings suggest that the early prediction of schizophrenia is feasible using a MRI-based linear SVM classifier operating at the single-subject level. Additionally, we have shown that the combination of baseline neuroanatomical data with measures of neurocognitive functioning and schizotypal cognition can improve predictive performance. The application of our pattern classification approach to baseline structural MRI data from the FePsy study highly replicated our previous findings. Our classification method identified spatially distributed networks that discriminate at baseline between subjects that later developed schizophrenia and other related psychoses and those that did not. Finally, a preliminary classification analysis using pooled datasets from the EHRS and the FePsy study supports the existence of a neuroanatomical pattern that differentiates between groups of high-risk subjects that develop psychosis against those who do not across research sites and despite any between-sites differences. Taken together, our findings suggest that machine learning is capable of distinguishing between cohorts of high risk subjects that later convert to psychosis and those that do not based on patterns of structural abnormalities that are present before disease onset. Our findings have some clinical implications in that machine learning-based approaches could advise or complement clinical decision-making in early intervention strategies in schizophrenia and related psychoses. Future work will be, however, required to tackle issues of reproducibility of early diagnostic biomarkers across research sites, where different assessment criteria and imaging equipment and protocols are used. In addition, future projects may also examine the diagnostic and prognostic value of multimodal neuroimaging data, possibly combined with other clinical, neurocognitive, genetic information.
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Pennington, Eva Patrice. "Brain-based learning theory the incorporation of movement to increase learning /." Lynchburg, Va. : Liberty University, 2010. http://digitalcommons.liberty.edu.
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Lee, Hyangsook. "The brain and learning| Examining the connection between brain activity, spatial intelligence, and learning outcomes in online visual instruction." Thesis, Kent State University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3618876.
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The purpose of the study was to compare 2D and 3D visual presentation styles, both still frame and animation, on subjects' brain activity measured by the amplitude of EEG alpha wave and on their recall to see if alpha power and recall differ significantly by depth and movement of visual presentation style and by spatial intelligence. In addition, the study sought to determine whether there is any significant interaction between spatial intelligence and visual presentation style on alpha power and recall, and to determine whether any relationship exists between alpha power and recall.
The subjects in the present study were one hundred and twenty three undergraduate students at a university in the Midwest. After taking Vandenberg & Kuse's Mental Rotations Test, subjects were divided into low and high spatial intelligence groups, and subjects in each spatial intelligence group were evenly assigned to four different types of visual presentation style (2D still frame, 2D animation, 3D still frame, and 3D animation), receiving an instruction on LASIK eye surgical procedure in its respective visual presentation style. During the one-minute visual instruction, subjects' brain activity was measured and recorded using a wireless EEG headset. Upon completion of the instruction, subjects were given a 10-item multiple-choice test to measure their recall of the material presented during the instruction.
Two 2 (spatial intelligence) x 2 (depth) x 2 (movement) factorial Analysis of Variance (ANOVA) were conducted, one with alpha power as a dependent variable and the other with recall as a dependent variable, to determine whether there is a significant difference in alpha power and recall by spatial intelligence and visual presentation style, as well as whether there is an interaction between these variables that affects alpha power and recall. The Pearson Correlation Coefficient was calculated to examine relationship between alpha power and recall.
The present study found (a) EEG alpha power did not differ by the difference in depth and movement, (b) 2D and animation were found to be more effective on recall, (c) alpha power did not differ by spatial intelligence, (d) recall did not differ by spatial intelligence, (e) there was a significant interaction between spatial intelligence and movement that affected alpha power; still frame resulted in higher alpha power for low spatial learners, and animation resulted in higher alpha power for high spatial learners, (f) there was a significant interaction between spatial intelligence, depth and movement on recall; for low spatial learners, 2D animation resulted in significantly higher recall than both 2D still frame and 3D animation, and for high spatial learners, 3D animation resulted in significantly higher recall than 3D still frame, and both 2D still frame and 2D animation resulted in close to significantly higher recall than 3D still frame, and (g) there was a mildly inverse relationship between alpha power and recall, brought on by a strong inverse relationship in 2D still frame revealing a 'higher alpha power-lower recall connection' for low spatial learners and a 'lower alpha power-higher recall connection' for high spatial learners.
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Lee, Hyangsook. "The Brain and Learning: Examining the Connection between Brain Activity, Spatial Intelligence, and Learning Outcomes in Online Visual Instruction." Kent State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=kent1380667253.
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Lake, Brenden M. "Towards more human-like concept learning in machines : compositionality, causality, and learning-to-learn." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/95856.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2014.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 211-220).
People can learn a new concept almost perfectly from just a single example, yet machine learning algorithms typically require hundreds or thousands of examples to perform similarly. People can also use their learned concepts in richer ways than conventional machine learning systems - for action, imagination, and explanation suggesting that concepts are far more than a set of features, exemplars, or rules, the most popular forms of representation in machine learning and traditional models of concept learning. For those interested in better understanding this human ability, or in closing the gap between humans and machines, the key computational questions are the same: How do people learn new concepts from just one or a few examples? And how do people learn such abstract, rich, and flexible representations? An even greater puzzle arises by putting these two questions together: How do people learn such rich concepts from just one or a few examples? This thesis investigates concept learning as a form of Bayesian program induction, where learning involves selecting a structured procedure that best generates the examples from a category. I introduce a computational framework that utilizes the principles of compositionality, causality, and learning-to-learn to learn good programs from just one or a handful of examples of a new concept. New conceptual representations can be learned compositionally from pieces of related concepts, where the pieces reflect real part structure in the underlying causal process that generates category examples. This approach is evaluated on a number of natural concept learning tasks where humans and machines can be compared side-by-side. Chapter 2 introduces a large-scale data set of novel, simple visual concepts for studying concept learning from sparse data. People were asked to produce new examples of over 1600 novel categories, revealing consistent structure in the generative programs that people used. Initial experiments also show that this structure is useful for one-shot classification. Chapter 3 introduces the computational framework called Hierarchical Bayesian Program Learning, and Chapters 4 and 5 compare humans and machines on six tasks that cover a range of natural conceptual abilities. On a challenging one-shot classification task, the computational model achieves human-level performance while also outperforming several recent deep learning models. Visual "Turing test" experiments were used to compare humans and machines on more creative conceptual abilities, including generating new category examples, predicting latent causal structure, generating new concepts from related concepts, and freely generating new concepts. In each case, fewer than twenty-five percent of judges could reliably distinguish the human behavior from the machine behavior, showing that the model can generalize in ways similar to human performance. A range of comparisons with lesioned models and alternative modeling frameworks reveal that three key ingredients - compositionality, causality, and learning-to-learn - contribute to performance in each of the six tasks. This conclusion is further supported by the results of Chapter 6, where a computational model using only two of these three principles was evaluated on the one-shot learning of new spoken words. Learning programs with these ingredients is a promising route towards more humanlike concept learning in machines.
by Brenden M. Lake.
Ph. D.
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Styles, Benjamin John. "Learning and sensory processing in a simple brain." Thesis, University of Sussex, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404208.
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The aim of this project was to locate and characterise sites of plasticity involved in long-term memory in a model invertebrate system, the snail Lymnaea stagnalis. Plastic changes are likely to involve integrating neurons in the sensory pathways that process the conditioned and reward stimuli used in chemical appetitive conditioning. The cerebral ganglia of the Lymnaea brain are a likely location for sensory integration making them a primary target for this investigation. A chemosensory nerve innervating the cerebral ganglia was dye-filled together with a nerve linking the cerebral ganglia to the feeding network. These experiments revealed specific sites of potential synaptic contact between the two nerves and subsequently, six new cerebral-buccal interneurons were identified and characterised electrophysiologically. Defining nitric oxide synthase distribution in peripheral and central neurons provided another route to finding sites of plasticity since nitric oxide is required for longterm memory formation in Lymnaea. Electrophysiological correlates of behavioural learning were found in the feeding motoneurons and the connective containing the cerebral-buccal interneurons. Variations in conditioned stimulus concentration and sites of its perfusion were discovered to be of crucial importance for the observation of these learning correlates. Determining the role of the CA1, CT2 and CV1 a interneurons in conditioned responses was a central aim. Evidence for their possible role in learning included: their response to the conditioning and reward stimuli in naive, conditioned and control animals; their role as modulatory interneurons of the feeding network and their anatomical and electrophysiological connectivity to primary sensory neurons and interneurons of the feeding network. A change in the response of the CA1 cell to the reward stimulus after conditioning was thought to arise due to pre-exposure to the conditioned stimulus. No individual neuronal change was found that could account for conditioned feeding responses in the whole network
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Alderman, Nicholas. "Maximising the learning potential of brain injured patients." Thesis, University of Southampton, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296354.
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Soltaninejad, Mohammadreza. "Supervised learning-based multimodal MRI brain image analysis." Thesis, University of Lincoln, 2017. http://eprints.lincoln.ac.uk/30883/.
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Medical imaging plays an important role in clinical procedures related to cancer, such as diagnosis, treatment selection, and therapy response evaluation. Magnetic resonance imaging (MRI) is one of the most popular acquisition modalities which is widely used in brain tumour analysis and can be acquired with different acquisition protocols, e.g. conventional and advanced. Automated segmentation of brain tumours in MR images is a difficult task due to their high variation in size, shape and appearance. Although many studies have been conducted, it still remains a challenging task and improving accuracy of tumour segmentation is an ongoing field. The aim of this thesis is to develop a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from multimodal MRI images. In this thesis, firstly, the whole brain tumour is segmented from fluid attenuated inversion recovery (FLAIR) MRI, which is commonly acquired in clinics. The segmentation is achieved using region-wise classification, in which regions are derived from superpixels. Several image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomised trees (ERT) classifies each superpixel into tumour and non-tumour. Secondly, the method is extended to 3D supervoxel based learning for segmentation and classification of tumour tissue subtypes in multimodal MRI brain images. Supervoxels are generated using the information across the multimodal MRI data set. This is then followed by a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The information from the advanced protocols of diffusion tensor imaging (DTI), i.e. isotropic (p) and anisotropic (q) components is also incorporated to the conventional MRI to improve segmentation accuracy. Thirdly, to further improve the segmentation of tumour tissue subtypes, the machine-learned features from fully convolutional neural network (FCN) are investigated and combined with hand-designed texton features to encode global information and local dependencies into feature representation. The score map with pixel-wise predictions is used as a feature map which is learned from multimodal MRI training dataset using the FCN. The machine-learned features, along with hand-designed texton features are then applied to random forests to classify each MRI image voxel into normal brain tissues and different parts of tumour. The methods are evaluated on two datasets: 1) clinical dataset, and 2) publicly available Multimodal Brain Tumour Image Segmentation Benchmark (BRATS) 2013 and 2017 dataset. The experimental results demonstrate the high detection and segmentation performance of the III single modal (FLAIR) method. The average detection sensitivity, balanced error rate (BER) and the Dice overlap measure for the segmented tumour against the ground truth for the clinical data are 89.48%, 6% and 0.91, respectively; whilst, for the BRATS dataset, the corresponding evaluation results are 88.09%, 6% and 0.88, respectively. The corresponding results for the tumour (including tumour core and oedema) in the case of multimodal MRI method are 86%, 7%, 0.84, for the clinical dataset and 96%, 2% and 0.89 for the BRATS 2013 dataset. The results of the FCN based method show that the application of the RF classifier to multimodal MRI images using machine-learned features based on FCN and hand-designed features based on textons provides promising segmentations. The Dice overlap measure for automatic brain tumor segmentation against ground truth for the BRATS 2013 dataset is 0.88, 0.80 and 0.73 for complete tumor, core and enhancing tumor, respectively, which is competitive to the state-of-the-art methods. The corresponding results for BRATS 2017 dataset are 0.86, 0.78 and 0.66 respectively. The methods demonstrate promising results in the segmentation of brain tumours. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. In the experiments, texton has demonstrated its advantages of providing significant information to distinguish various patterns in both 2D and 3D spaces. The segmentation accuracy has also been largely increased by fusing information from multimodal MRI images. Moreover, a unified framework is present which complementarily integrates hand-designed features with machine-learned features to produce more accurate segmentation. The hand-designed features from shallow network (with designable filters) encode the prior-knowledge and context while the machine-learned features from a deep network (with trainable filters) learn the intrinsic features. Both global and local information are combined using these two types of networks that improve the segmentation accuracy.
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Mahbod, Amirreza. "Structural Brain MRI Segmentation Using Machine Learning Technique." Thesis, KTH, Skolan för teknik och hälsa (STH), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189985.
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Segmenting brain MR scans could be highly benecial for diagnosing, treating and evaluating the progress of specic diseases. Up to this point, manual segmentation,performed by experts, is the conventional method in hospitals and clinical environments. Although manual segmentation is accurate, it is time consuming, expensive and might not be reliable. Many non-automatic and semi automatic methods have been proposed in the literature in order to segment MR brain images, but the levelof accuracy is not comparable with manual segmentation. The aim of this project is to implement and make a preliminary evaluation of a method based on machine learning technique for segmenting gray matter (GM),white matter (WM) and cerebrospinal uid (CSF) of brain MR scans using images available within the open MICCAI grand challenge (MRBrainS13).The proposed method employs supervised articial neural network based autocontext algorithm, exploiting intensity-based, spatial-based and shape model-basedlevel set segmentation results as features of the network. The obtained average results based on Dice similarity index were 97.73%, 95.37%, 82.76%, 88.47% and 84.78% for intracranial volume, brain (WM + GM), CSF, WM and GM respectively. This method achieved competitive results with considerably shorter required training time in MRBrainsS13 challenge.
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Karlaftis, Vasileios Misak. "Structural and functional brain plasticity for statistical learning." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/278790.
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Extracting structure from initially incomprehensible streams of events is fundamental to a range of human abilities: from navigating in a new environment to learning a language. These skills rely on our ability to extract spatial and temporal regularities, often with minimal explicit feedback, that is known as statistical learning. Despite the importance of statistical learning for making perceptual decisions, we know surprisingly little about the brain circuits and how they change when learning temporal regularities. In my thesis, I combine behavioural measurements, Diffusion Tensor Imaging (DTI) and resting-state fMRI (rs-fMRI) to investigate the structural and functional circuits that are involved in statistical learning of temporal structures. In particular, I compare structural connectivity as measured by DTI and functional connectivity as measured by rs-fMRI before vs. after training to investigate learning-dependent changes in human brain pathways. Further, I combine the two imaging modalities using graph theory and regression analyses to identify key predictors of individual learning performance. Using a prediction task in the context of sequence learning without explicit feedback, I demonstrate that individuals adapt to the environment’s statistics as they change over time from simple repetition to probabilistic combinations. Importantly, I show that learning of temporal structures relates to decision strategy that varies among individuals between two prototypical distributions: matching the exact sequence statistics or selecting the most probable outcome in a given context (i.e. maximising). Further, combining DTI and rs-fMRI, I show that learning-dependent plasticity in dissociable cortico-striatal circuits relates to decision strategy. In particular, matching relates to connectivity between visual cortex, hippocampus and caudate, while maximisation relates to connectivity between frontal and motor cortices and striatum. These findings have potential translational applications, as alternate brain routes may be re-trained to support learning ability when specific pathways (e.g. memory-related circuits) are compromised by age or disease.
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Fernandes, José Joaquim Fonseca Ribas. "Hierarchical Reinforcement Learning in Behavior and the Brain." Doctoral thesis, Universidade Nova de Lisboa. Instituto de Tecnologia química e Biológica, 2013. http://hdl.handle.net/10362/11971.
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Dissertation presented to obtain the Ph.D degree in Biology, NeuroscienceReinforcement learning (RL) has provided key insights to the neurobiology of learning and decision making. The pivotal nding is that the phasic activity of dopaminergic cells in the ventral tegmental area during learning conforms to a reward prediction error (RPE), as speci ed in the temporal-di erence learning algorithm (TD). This has provided insights to conditioning, the distinction between habitual and goal-directed behavior, working memory, cognitive control and error monitoring. It has also advanced the understanding of cognitive de cits in Parkinson's disease, depression, ADHD and of personality traits such as impulsivity.(...)
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Evanshen, Pamela. "Rating the Learning Environment for Brain Compatible Elements." Digital Commons @ East Tennessee State University, 2004. https://dc.etsu.edu/etsu-works/4412.
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Laflamme, Denise Marie. "The brain-based theory of learning and multimedia." CSUSB ScholarWorks, 1994. https://scholarworks.lib.csusb.edu/etd-project/1002.
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For this project the brain-based theory of learning, an eclectic theory that incorporates the cognitive and humanistic views was researched. Multimedia, a technology which supports the principles of brain-based learning, was then selected as the vehicle to present historical materials to students.
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Joos, Louis. "Deformable 3D Brain MRI Registration with Deep Learning." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262852.
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Traditional deformable registration methods have achieved impressive performances but are computationally time-consuming since they have to optimize an objective function for each new pair of images. Very recently some learning-based approaches have been proposed to enable fast registration by learning to estimate the spatial transformation parameters directly from the input images. Here we present a method for 3D fast pairwise registration of brain MR images. We model the deformation function with B-splines and learn the optimal control points using a U-Net like CNN architecture. An inverse-consistency loss has been used to enforce diffeomorphicity of the deformation. The proposed algorithm does not require supervised information such as segmented labels but some can be used to help the registration process. We also implemented several strategies to account for the multi-resolution nature of the problem. The method has been evaluated on MICCAI 2012 brain MRI datasets, and evaluated on both similarity and invertibility of the computed transformation.
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Leonard, Julia Anne Ph D. Massachusetts Institute of Technology. "Social influences on children's learning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120622.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 129-170).
Adults greatly impact children's learning: they serve as models of how to behave, and as parents, provide the larger social context in which children grow up. This thesis explores how adults impact children's learning across two time scales. Chapters 2 and 3 ask how a brief exposure to an adult model impacts children's moment-to-moment approach towards learning, and Chapters 4 and 5 look at how children's long-term social context impacts their brain development and capacity to learn. In Chapter 2, I show that preschool-age children integrate information from adults' actions, outcomes, and testimony to decide how hard to try on novel tasks. Children persist the longest when adults practice what they preach: saying they value effort, or giving children a pep talk, in conjunction with demonstrating effortful success on their own task. Chapter 3 demonstrates that social learning about effort is present in the first year of life and generalizes across tasks. In Chapter 4, I find that adolescents' long-term social environments have a selective impact on neural structure and function: socioeconomic-status (SES) relates to hippocampal-prefrontal declarative memory, but not striatal-dependent procedural memory. Finally, in Chapter 5 I demonstrate that the neural correlates of fluid reasoning differ by SES, suggesting that positive brain development varies by early life environment. Collectively, this work elucidates both the malleable social factors that positively impact children's learning and the unique neural and cognitive adaptations that children develop in response to adverse environments.
by Julia Anne Leonard.
Ph. D.
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Brashers-Krug, Thomas M. (Thomas More). "Consolidation in human motor learning." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11884.
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Frank, Michael C. Ph D. Massachusetts Institute of Technology. "Early word learning through communicative inference." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62045.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 109-122).
How do children learn their first words? Do they do it by gradually accumulating information about the co-occurrence of words and their referents over time, or are words learned via quick social inferences linking what speakers are looking at, pointing to, and talking about? Both of these conceptions of early word learning are supported by empirical data. This thesis presents a computational and theoretical framework for unifying these two different ideas by suggesting that early word learning can best be described as a process of joint inferences about speakers' referential intentions and the meanings of words. Chapter 1 describes previous empirical and computational research on "statistical learning"--the ability of learners to use distributional patterns in their language input to learn about the elements and structure of language-and argues that capturing this abifity requires models of learning that describe inferences over structured representations, not just simple statistics. Chapter 2 argues that social signals of speakers' intentions, even eye-gaze and pointing, are at best noisy markers of reference and that in order to take advantage of these signals fully, learners must integrate information across time. Chapter 3 describes the kinds of inferences that learners can make by assuming that speakers are informative with respect to their intended meaning, introducing and testing a formalization of how Grice's pragmatic maxims can be used for word learning. Chapter 4 presents a model of cross-situational intentional word learning that both learns words and infers speakers' referential intentions from labeled corpus data.
by Michael C. Frank.
Ph.D.
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Frogner, Charles (Charles Albert). "Learning and inference with Wasserstein metrics." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120619.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 131-143).
This thesis develops new approaches for three problems in machine learning, using tools from the study of optimal transport (or Wasserstein) distances between probability distributions. Optimal transport distances capture an intuitive notion of similarity between distributions, by incorporating the underlying geometry of the domain of the distributions. Despite their intuitive appeal, optimal transport distances are often difficult to apply in practice, as computing them requires solving a costly optimization problem. In each setting studied here, we describe a numerical method that overcomes this computational bottleneck and enables scaling to real data. In the first part, we consider the problem of multi-output learning in the presence of a metric on the output domain. We develop a loss function that measures the Wasserstein distance between the prediction and ground truth, and describe an efficient learning algorithm based on entropic regularization of the optimal transport problem. We additionally propose a novel extension of the Wasserstein distance from probability measures to unnormalized measures, which is applicable in settings where the ground truth is not naturally expressed as a probability distribution. We show statistical learning bounds for both the Wasserstein loss and its unnormalized counterpart. The Wasserstein loss can encourage smoothness of the predictions with respect to a chosen metric on the output space. We demonstrate this property on a real-data image tagging problem, outperforming a baseline that doesn't use the metric. In the second part, we consider the probabilistic inference problem for diffusion processes. Such processes model a variety of stochastic phenomena and appear often in continuous-time state space models. Exact inference for diffusion processes is generally intractable. In this work, we describe a novel approximate inference method, which is based on a characterization of the diffusion as following a gradient flow in a space of probability densities endowed with a Wasserstein metric. Existing methods for computing this Wasserstein gradient flow rely on discretizing the underlying domain of the diffusion, prohibiting their application to problems in more than several dimensions. In the current work, we propose a novel algorithm for computing a Wasserstein gradient flow that operates directly in a space of continuous functions, free of any underlying mesh. We apply our approximate gradient flow to the problem of filtering a diffusion, showing superior performance where standard filters struggle. Finally, we study the ecological inference problem, which is that of reasoning from aggregate measurements of a population to inferences about the individual behaviors of its members. This problem arises often when dealing with data from economics and political sciences, such as when attempting to infer the demographic breakdown of votes for each political party, given only the aggregate demographic and vote counts separately. Ecological inference is generally ill-posed, and requires prior information to distinguish a unique solution. We propose a novel, general framework for ecological inference that allows for a variety of priors and enables efficient computation of the most probable solution. Unlike previous methods, which rely on Monte Carlo estimates of the posterior, our inference procedure uses an efficient fixed point iteration that is linearly convergent. Given suitable prior information, our method can achieve more accurate inferences than existing methods. We additionally explore a sampling algorithm for estimating credible regions.
by Charles Frogner.
Ph. D.
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Tenenbaum, Joshua B. (Joshua Brett) 1972. "A Bayesian framework for concept learning." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/16714.
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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1999.Includes bibliographical references (p. 297-314).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Human concept learning presents a version of the classic problem of induction, which is made particularly difficult by the combination of two requirements: the need to learn from a rich (i.e. nested and overlapping) vocabulary of possible concepts and the need to be able to generalize concepts reasonably from only a few positive examples. I begin this thesis by considering a simple number concept game as a concrete illustration of this ability. On this task, human learners can with reasonable confidence lock in on one out of a billion billion billion logically possible concepts, after seeing only four positive examples of the concept, and can generalize informatively after seeing just a single example. Neither of the two classic approaches to inductive inference hypothesis testing in a constrained space of possible rules and computing similarity to the observed examples can provide a complete picture of how people generalize concepts in even this simple setting. This thesis proposes a new computational framework for understanding how people learn concepts from examples, based on the principles of Bayesian inference. By imposing the constraints of a probabilistic model of the learning situation, the Bayesian learner can draw out much more information about a concept's extension from a given set of observed examples than either rule-based or similarity-based approaches do, and can use this information in a rational way to infer the probability that any new object is also an instance of the concept. There are three components of the Bayesian framework: a prior probability distribution over a hypothesis space of possible concepts; a likelihood function, which scores each hypothesis according to its probability of generating the observed examples; and the principle of hypothesis averaging, under which the learner computes the probability of generalizing a concept to new objects by averaging the predictions of all hypotheses weighted by their posterior probability (proportional to the product of their priors and likelihoods). The likelihood, under the assumption of randomly sampled positive examples, embodies the size principle for scoring hypotheses: smaller consistent hypotheses are more likely than larger hypotheses, and they become exponentially more likely as the number of observed examples increases. The principle of hypothesis averaging allows the Bayesian framework to accommodate both rule-like and similarity-like generalization behavior, depending on how peaked the posterior probability is. Together, the size principle plus hypothesis averaging predict a convergence from similarity-like generalization (due to a broad posterior distribution) after very few examples are observed to rule-like generalization (due to a sharply peaked posterior distribution) after sufficiently many examples have been observed. The main contributions of this thesis are as follows. First and foremost, I show how it is possible for people to learn and generalize concepts from just one or a few positive examples (Chapter 2). Building on that understanding, I then present a series of case studies of simple concept learning situations where the Bayesian framework yields both qualitative and quantitative insights into the real behavior of human learners (Chapters 3-5). These cases each focus on a different learning domain. Chapter 3 looks at generalization in continuous feature spaces, a typical representation of objects in psychology and machine learning with the virtues of being analytically tractable and empirically accessible, but the downside of being highly abstract and artificial. Chapter 4 moves to the more natural domain of learning words for categories of objects and shows the relevance of the same phenomena and explanatory principles introduced in the more abstract setting of Chapters 1-3 for real-world learning tasks like this one. In each of these domains, both similarity-like and rule-like generalization emerge as special cases of the Bayesian framework in the limits of very few or very many examples, respectively. However, the transition from similarity to rules occurs much faster in the word learning domain than in the continuous feature space domain. I propose a Bayesian explanation of this difference in learning curves that places crucial importance on the density or sparsity of overlapping hypotheses in the learner's hypothesis space. To test this proposal, a third case study (Chapter 5) returns to the domain of number concepts, in which human learners possess a more complex body of prior knowledge that leads to a hypothesis space with both sparse and densely overlapping components. Here, the Bayesian theory predicts and human learners produce either rule-based or similarity-based generalization from a few examples, depending on the precise examples observed. I also discusses how several classic reasoning heuristics may be used to approximate the much more elaborate computations of Bayesian inference that this domain requires. In each of these case studies, I confront some of the classic questions of concept learning and induction: Is the acquisition of concepts driven mainly by pre-existing knowledge or the statistical force of our observations? Is generalization based primarily on abstract rules or similarity to exemplars? I argue that in almost all instances, the only reasonable answer to such questions is, Both. More importantly, I show how the Bayesian framework allows us to answer much more penetrating versions of these questions: How does prior knowledge interact with the observed examples to guide generalization? Why does generalization appear rule-based in some cases and similarity-based in others? Finally, Chapter 6 summarizes the major contributions in more detailed form and discusses how this work ts into the larger picture of contemporary research on human learning, thinking, and reasoning.
by Joshua B. Tenenbaum.
Ph.D.
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Piantadosi, Steven Thomas. "Learning and the language of thought." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68423.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 179-191).
This thesis develops the hypothesis that key aspects of learning and development can be understood as rational statistical inferences over a compositionally structured representation system, a language of thought (LOT) (Fodor, 1975). In this setup, learners have access to a set of primitive functions and learning consists of composing these functions in order to created structured representations of complex concepts. We present an inductive statistical model over these representations that formalizes an optimal Bayesian trade-off between representational complexity and fit to the observed data. This approach is first applied to the case of number-word acquisition, for which statistical learning with a LOT can explain key developmental patterns and resolve philosophically troublesome aspects of previous developmental theories. Second, we show how these same formal tools can be applied to children's acquisition of quantifiers. The model explains how children may achieve adult competence with quantifiers' literal meanings and presuppositions, and predicts several of the most-studied errors children make while learning these words. Finally, we model adult patterns of generalization in a massive concept-learning experiment. These results provide evidence for LOT models over other approaches and provide quantitative evaluation of different particular LOTs.
by Steven Thomas Piantadosi.
Ph.D.
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Ellis, Kevin Ph D. (Kevin M. )Massachusetts Institute of Technology. "Algorithms for learning to induce programs." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/130184.
Full textAbstract:
Thesis: Ph. D. in Cognitive Science, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, September, 2020Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 213-224).
The future of machine learning should have a knowledge representation that supports, at a minimum, several features: Expressivity, interpretability, the potential for reuse by both humans and machines, while also enabling sample-efficient generalization. Here we argue that programs-i.e., source code-are a knowledge representation which can contribute to the project of capturing these elements of intelligence. This research direction however requires new program synthesis algorithms which can induce programs solving a range of AI tasks. This program induction challenge confronts two primary obstacles: the space of all programs is infinite, so we need a strong inductive bias or prior to steer us toward the correct programs; and even if we have that prior, effectively searching through the vast combinatorial space of all programs is generally intractable. We introduce algorithms that learn to induce programs, with the goal of addressing these two primary obstacles. Focusing on case studies in vision, computational linguistics, and learning-to-learn, we develop an algorithmic toolkit for learning inductive biases over programs as well as learning to search for programs, drawing on probabilistic, neural, and symbolic methods. Together this toolkit suggests ways in which program induction can contribute to AI, and how we can use learning to improve program synthesis technologies.
by Kevin Ellis.
Ph. D. in Cognitive Science
Ph.D.inCognitiveScience Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences
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Blum, Julia Maria. "Coherent brain oscillations during processes of human sensorimotor learning /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17951.
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Gonzalez, Claudia Cristina. "Linking brain and behaviour in motor sequence learning tasks." Thesis, University of Leeds, 2012. http://etheses.whiterose.ac.uk/3603/.
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Sequence learning is a fundamental brain function that allows for the acquisition of a wide range of skills. Unlearned movements become faster and more accurate with repetition, due to a process called prediction. Predictive behaviour observed in the eye and hand compensates for the inherent temporal delays in the sensorimotor system and allows for the generation of motor actions prior to visual guidance. We investigated predictive behaviour and the brain areas associated with this processing in (i) the oculomotor system (Eye Only (EO): saccade vs. pursuit) and (ii) during eye and hand coordination (EH). Participants were asked to track a continuous moving target in predictable or random sequence conditions. EO and EH experiments were divided into 1) EO behavioural and 2) EO fMRI findings, and 3) EH behavioural and 4) EH fMRI findings. Results provide new insights into how individuals predict when learning a sequence of target movements, which is not limited to short--‐term memory capacities and that forms a link between shorter and longer--‐term motor skill learning. Furthermore, brain imaging results revealed distinct levels of activation within and between brain areas for repeated and randomized sequences that reflect the distinct timing threshold and adaptation levels needed for the two oculomotor systems. EH results revealed similar predictive behaviour in the eye and the hand, but also demonstrated enhanced coupling between the two motor systems during sequence learning. EH brain imaging findings have provided novel insights into the brain areas involved in coordination, and those areas more associated with sequence learning. Results show evidence of common predictive networks used for the eye and hand during learning.
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Tompkins, Abreena Walker. "Brain-based learning theory an online course design model /." Lynchburg, Va. : Liberty University, 2007. http://digitalcommons.liberty.edu.
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Frangou, Polytimi. "Inhibitory mechanisms for visual learning in the human brain." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/280767.
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Identifying targets in cluttered scenes is critical for our interactions in complex environments. Our visual system is challenged to both detect elusive targets that we may want to avoid or chase and discriminate between targets that are highly similar. These tasks require our visual system to become an expert at detecting distinctive features that help us differentiate between indistinguishable targets. As the human brain is trained on this type of visual tasks, we observe changes in its function that correspond to improved performance. We use functional brain imaging, to measure learning-dependent modulations of brain activation and investigate the processes that mediate functional brain plasticity. I propose that dissociable brain mechanisms are engaged when detecting targets in clutter vs. discriminating between highly similar targets: for the former, background clutter needs to be suppressed for the target to be recognised, whereas for the latter, neurons are tuned to respond to fine differences. Although GABAergic inhibition is known to suppress redundant neuronal populations and tune neuronal representations, its role in visual learning remains largely unexplored. Here, I propose that GABAergic inhibition plays an important role in visual plasticity through training on these tasks. The purpose of my PhD is to investigate the inhibitory mechanisms that mediate visual perceptual learning; in particular, learning to detect patterns in visual clutter and discriminate between highly similar patterns. I show that BOLD signals as measured by functional Magnetic Resonance Imaging (fMRI) do not differentiate between the two proposed mechanisms. In contrast, Magnetic Resonance Spectroscopy (MRS) provides strong evidence for the distinct involvement of GABAergic inhibition in visual plasticity. Further, my findings show GABA changes during the time-course of learning providing evidence for a distinct role of GABA in learning-dependent plasticity across different brain regions involved in visual learning. Finally, I test the causal link between inhibitory contributions and visual plasticity using a brain stimulation intervention that perturbs the excitation-inhibition balance in the visual cortex and facilitates learning.
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Scholz, Jan. "Structural brain plasticity : Individual differences and changes with learning." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.533876.
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Nguyen, Dieu My Thanh. "OLFACTORY LEARNING AND BRAIN ACTIVITY IN NOVOMESSOR COCKERELLI ANTS." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613353.
Full textAbstract:
In this study, an olfactory associative conditioning paradigm was developed to study the
learning and memory capabilities of Novomessor cockerelli ants. When the antennae of the
ant come into contact with sucrose solution, the ant extends its tongue to consume the
sucrose. The tongue reflex was conditioned by pairing an odor (conditioned stimulus) with
sucrose (unconditioned stimulus) over ten trials. The ant’s tongue reflex in response to
odor indicates that an association between the odor and the sucrose has been made in the
ant’s brain. The second part of the study includes analyzing the regional brain variations of
cytochrome oxidase (COX) staining after olfactory conditioning. The antennal lobe and
mushroom body are major brain regions in the insect olfactory pathway, and are regions of
interest in this analysis. Results show that there are significant differences in metabolic
activity across brain regions (antennal lobe, glomeruli, central boxy complex, mushroom
body, and lateral protocerebrum), but the differences do not correlate with the learning
status of the ants (learned vs non-learned).
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Lau, Kiu Wai. "Representation Learning on Brain MR Images for Tumor Segmentation." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234827.
Full textAbstract:
MRI is favorable for brain imaging due to its excellent soft tissue contrast and absence of harmful ionizing radiation. Many have proposed supervised multimodal neural networks for automatic brain tumor segmentation and showed promising results. However, they rely on large amounts of labeled data to generalize well. The trained network is also highly specific to the task and input. Missing inputs will most likely have a detrimental effect on the network’s predictions, if it works at all. The aim of this thesis work is to implement a deep neural network that learns the general representation of multimodal MRI images in an unsupervised manner and is insensitive to missing modalities. With the latent representation, labeled data are then used for brain tumor segmentation. A variational autoencoder and an unified representation network are used for repre- sentation learning. Fine-tuning or joint training was used for segmentation task. The performances of the algorithms at the reconstruction task was evaluated using the mean- squared error and at the segmentation task using the Dice coefficient. Both networks demonstrated the possibility in learning brain MR representations, but the unified representation network was more successful at the segmentation task.
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Rose, Rickie Lou. "The connection of brain compatible learning theory and leadership." [Bloomington, Ind.] : Indiana University, 2005. http://wwwlib.umi.com/dissertations/fullcit/3175993.
Full textAbstract:
Thesis (Ed.D.)--Indiana University, School of Education, 2005.Title from PDF t.p. (viewed Dec. 8, 2008). Source: Dissertation Abstracts International, Volume: 66-05, Section: A, page: 1587. Adviser: L. Burello.
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Astolfi, Pietro. "Toward the "Deep Learning" of Brain White Matter Structures." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/337629.
Full textAbstract:
In the brain, neuronal cells located in different functional regions communicate through a dense structural network of axons known as the white matter (WM) tissue. Bundles of axons that share similar pathways characterize the WM anatomy, which can be investigated in-vivo thanks to the recent advances of magnetic resonance (MR) techniques. Diffusion MR imaging combined with tractography pipelines allows for a virtual reconstruction of the whole WM anatomy of in-vivo brains, namely the tractogram. It consists of millions of WM fibers as 3D polylines, each approximating thousands of axons. From the analysis of a tractogram, neuroanatomists can characterize well-known white matter structures and detect anatomically non-plausible fibers, which are artifacts of the tractography and often constitute a large portion of it. The accurate characterization of tractograms is pivotal for several clinical and neuroscientific applications. However, such characterization is a complex and time-consuming process that is difficult to be automatized as it requires properly encoding well-known anatomical priors. In this thesis, we propose to investigate the encoding of anatomical priors with a supervised deep learning framework. The ultimate goal is to reduce the presence of artifactual fibers to enable a more accurate automatic process of WM characterization. We devise the problem by distinguishing between volumetric and non-volumetric representations of white matter structures. In the first case, we learn the segmentation of the WM regions that represent relevant anatomical waypoints not yet classified by WM atlases. We investigate using Convolutional Neural Networks (CNNs) to exploit the volumetric representation of such priors. In the second case, the goal is to learn from the 3D polyline representation of fibers where the typical CNN models are not suitable. We introduce the novelty of using Geometric Deep Learning (GDL) models designed to process data having an irregular representation. The working assumption is that the geometrical properties of fibers are informative for the detection of tractogram artifacts. As a first contribution, we present StemSeg that extends the use of CNNs to detect the WM portion representing the waypoints of all the fibers for a specific bundle. This anatomical landmark, called stem, can be critical for extracting that bundle. We provide the results of an empirical analysis focused on the Inferior Fronto-Occipital Fasciculus (IFOF). The effective segmentation of the stem improves the final segmentation of the IFOF, outperforming with a significant gap the reference state of the art. As a second and major contribution, we present Verifyber, a supervised tractogram filtering approach based on GDL, distinguishing between anatomically plausible and non-plausible fibers. The proposed model is designed to learn anatomical features directly from the fiber represented as a 3D points sequence. The extended empirical analysis on healthy and clinical subjects reveals multiple benefits of Verifyber: high filtering accuracy, low inference time, flexibility to different plausibility definitions, and good generalization. Overall, this thesis constitutes a step toward characterizing white matter using deep learning. It provides effective ways of encoding anatomical priors and an original deep learning model designed for fiber.