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Handayani, Baiq Sri, i A. D. Corebima. "Model brain based learning (BBL) and whole brain teaching (WBT) in learning". International Journal of Science and Applied Science: Conference Series 1, nr 2 (14.08.2017): 153. http://dx.doi.org/10.20961/ijsascs.v1i2.5142.
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<p class="Abstract">The learning process is a process of change in behavior as a form of the result of learning. The learning model is a crucial component of the success of the learning process. The learning model is growing fastly, and each model has different characteristics. Teachers are required to be able to understand each model to teach the students optimally by matching the materials and the learning model. The best of the learning model is the model that based on the brain system in learning that are the model of Brain Based Learning (BBL) and the model of Whole Brain Teaching (WBT). The purposes of this article are to obtain information related to (1) the brain’s natural learning system, (2) analyze the characteristics of the model BBL and WBT based on theory, brain sections that play a role associated with syntax, similarities, and differences, (3) explain the distinctive characteristics of both models in comparison to other models. The results of this study are: (1) the brain’s natural learning system are: (a) the nerves in each hemisphere do not work independently, (b) doing more activities can connect more brain nerves, (c) the right hemisphere controls the left side motoric sensor of the body, and vice versa; (2) the characteristics of BBL and WBT are: (a) BBL is based on the brain’s structure and function, while the model WBT is based on the instructional approach, neurolinguistic, and body language, (b) the parts of the brain that work in BBL are: cerebellum, cerebral cortex, frontal lobe, limbic system, and prefrontal cortex; whereas the parts that work WBT are: prefrontal cortex, visual cortex, motor cortex, limbic system, and amygdala, (c) the similarities between them are that they both rely on the brain’s system and they both promote gesture in learning, whereas the differences are on the view of the purposes of gestures and the learning theory that they rely on. BBL relies on cognitive theory while WBT relies on social theory; (3) the typical attribute of them compared to other models are that in BBL there are classical music and gestures in the form of easy exercises, while on the WBT model there are fast instructions and movements as instructions or code of every spoken word.</p>
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Lucas, Caro, Danial Shahmirzadi i Nima Sheikholeslami. "Introducing Belbic: Brain Emotional Learning Based Intelligent Controller". Intelligent Automation & Soft Computing 10, nr 1 (styczeń 2004): 11–21. http://dx.doi.org/10.1080/10798587.2004.10642862.
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Sesmiarni, Zulfani. "Brain Based Teaching Model as Transformation of Learning Paradigm in Higher Education". Al-Ta lim Journal 22, nr 3 (10.12.2015): 266–75. http://dx.doi.org/10.15548/jt.v22i3.141.
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Brain -based teaching model is a new paradigm that can facilitate students in optimizing student learning by the functioning the brain as a whole. Lessons are held today assume that all students equally so that learning provide the same services to each student in the class. With this model, the students are given different stimulation according to their abilities and needs. Base on brain learning theory -based teaching, the learning should pay attention to the five needs of the brain in general. The fifth factor is the need for a sense of comfort, the need for interaction, the need for knowledge, the need for the activity and the need for self-reflection. All these needs will be connected if the lecturers able to present emotional learning, social learning, cognitive learning, physical learning and teaching reflection. Key Word : Instrucetional, Brain Based teaching, Learning.Copyright © 2015 by Al-Ta'lim All right reserved
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Kovalenko, Nataliya. "Astronomy: learning theories applicable for education in planetarium environment". EPJ Web of Conferences 200 (2019): 01014. http://dx.doi.org/10.1051/epjconf/201920001014.
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How do people learn in general and study astronomy in particular? To develop a coherent educational policy we need an appropriate theory. Does learning consist of the incremental addition of individual “bits” of information into the mind? Or is learning an active process that transforms the mind of the learner? Among different theories on how people learn are: Behaviorism, Neuroscience, Right Brain vs. Left Brain, Communities of Practice, Control Theory, Observational Learning (Social learning theory), Vygotsky and Social Cognition, Learning Styles, Piaget's theory, Constructivism, Brain-based Learning, Multiple Intelligences. These theories are described in brief. All of the above mentioned learning theories may be applicable to some extent in the case of astronomy education in a planetarium environment. Especially the Multiple Intelligences theory can be tested perfectly while teaching in Planetarium and thus should be taken into more thorough consideration. It is discussed what a planetarium may offer to the audience with different types of intelligences, according to the Multiple Intelligences approach.
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AlGhraibeh, Ahmad Mohamed Awad. "Learning and Thinking Styles Based on Whole Brain Theory in Relation to Emotional Intelligence". OALib 02, nr 05 (2015): 1–14. http://dx.doi.org/10.4236/oalib.1101436.
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Sesmiarni, Zulfani. "Model Brain Based Teaching Sebagai Transformasi Paradigma Pembelajaran di Perguruan Tinggi". Tadris: Jurnal Keguruan dan Ilmu Tarbiyah 1, nr 2 (19.12.2016): 93. http://dx.doi.org/10.24042/tadris.v1i2.1059.
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Brain-based teaching model is a new paradigm that can facilities students in optimal study by using the whole brain function of the students. The common learning that we deal today is concern that all students is equal so that the learning give the same treatment to all students in class. Therefore, with this learning model students are given different stimulus based on their ability and needed. So, based on learning theory of brain-based teaching the learning must give attention on five brain needed on general. Those five factors are the need of comfortable, the need of interact, the need of knowledge, the need of activity and the need of self-reflection. All of those factors can be fulfilled if the lecturer able to apply emotional learning, social learning, cognitive learning, physical learning, and reflection learning.Model pembelajaran brain based teaching adalah sebuah paradigma baru yang dapat memfasilitasi peserta didik dalam optimalisasi pembelajaran dengan menggunakan fungsi keseluruhan otak pada peserta didik. Pembelajaran umum yang kita hadapi saat ini menitik beratkan pada persamaan dalam memandang peserta didik yang kemudian pembelajaran hanya memberikan perlakuan yang sama kepada peserta didik dalam satu kelas. Oleh karena itu, dengan model pembelajaran seperti ini diberikan rangsangan yang berbeda berdasarkan kemampuan mereka dan kebutuh. Jadi, berdasarkan teori brain based teaching pendidik harus memberikan perhatian pada lima aspek yang dibutuhkan secara umum. Kelima faktor tersebut adalah: kebutuhan kenyamanan, kebutuhan bagaimana berinteraksi, kebutuhan ilmu pengetahuan, kebutuhan beraktivitas dan kebutuhan merefleksi diri. Semua faktor tersebut dapat terpenuhi jika pendidik mampu menerapkan pembelajaran emosional, pembelajaran sosial, pembelajaran kognitif, pembelajaran fisik, dan pembelajaran yang bersifat reflektif.Keywords: brain-based teaching, teaching model, a new paradigm
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Fitch, W. Tecumseh, i Angela D. Friederici. "Artificial grammar learning meets formal language theory: an overview". Philosophical Transactions of the Royal Society B: Biological Sciences 367, nr 1598 (19.07.2012): 1933–55. http://dx.doi.org/10.1098/rstb.2012.0103.
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Formal language theory (FLT), part of the broader mathematical theory of computation, provides a systematic terminology and set of conventions for describing rules and the structures they generate, along with a rich body of discoveries and theorems concerning generative rule systems. Despite its name, FLT is not limited to human language, but is equally applicable to computer programs, music, visual patterns, animal vocalizations, RNA structure and even dance. In the last decade, this theory has been profitably used to frame hypotheses and to design brain imaging and animal-learning experiments, mostly using the ‘artificial grammar-learning’ paradigm. We offer a brief, non-technical introduction to FLT and then a more detailed analysis of empirical research based on this theory. We suggest that progress has been hampered by a pervasive conflation of distinct issues, including hierarchy, dependency, complexity and recursion. We offer clarifications of several relevant hypotheses and the experimental designs necessary to test them. We finally review the recent brain imaging literature, using formal languages, identifying areas of convergence and outstanding debates. We conclude that FLT has much to offer scientists who are interested in rigorous empirical investigations of human cognition from a neuroscientific and comparative perspective.
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Lakoff, George. "The role of the brain in the metaphorical mathematical cognition". Behavioral and Brain Sciences 31, nr 6 (grudzień 2008): 658–59. http://dx.doi.org/10.1017/s0140525x08005748.
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AbstractRips et al. appear to discuss, and then dismiss with counterexamples, the brain-based theory of mathematical cognition given in Lakoff and Núñez (2000). Instead, they present another theory of their own that they correctly dismiss. Our theory is based on neural learning. Rips et al. misrepresent our theory as being directly about real-world experience and mappings directly from that experience.
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Cao, Benchun, Yanchun Liang, Shinichi Yoshida i Renchu Guan. "Facial Expression Decoding based on fMRI Brain Signal". International Journal of Computers Communications & Control 14, nr 4 (5.08.2019): 475–88. http://dx.doi.org/10.15837/ijccc.2019.4.3433.
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The analysis of facial expressions is a hot topic in brain-computer interface research. To determine the facial expressions of the subjects under the corresponding stimulation, we analyze the fMRI images acquired by the Magnetic Resonance. There are six kinds of facial expressions: "anger", "disgust", "sadness", "happiness", "joy" and "surprise". We demonstrate that brain decoding is achievable through the parsing of two facial expressions ("anger" and "joy"). Support vector machine and extreme learning machine are selected to classify these expressions based on time series features. Experimental results show that the classification performance of the extreme learning machine algorithm is better than support vector machine. Among the eight participants in the trials, the classification accuracy of three subjects reached 70-80%, and the remaining five subjects also achieved accuracy of 50-60%. Therefore, we can conclude that the brain decoding can be used to help analyzing human facial expressions.
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Miller, Raissa. "Neuroeducation: Integrating Brain-Based Psychoeducation into Clinical Practice". Journal of Mental Health Counseling 38, nr 2 (1.04.2016): 103–15. http://dx.doi.org/10.17744/mehc.38.2.02.
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Understanding and integrating neuroscience research into clinical practice represents a rapidly growing area in mental health. An expanding body of neuroscience literature increasingly informs clinical practice by validating theory, guiding clinical assessment and conceptualization, directing effective interventions, and facilitating cross-disciplinary communication. Little attention, however, has been given to the use of neuroeducation with clients. In this article, the author provides mental health counselors with a definition of neuroeducation and a rationale for incorporating neuroeducation into clinical practice. The author identifies common neuroeducation topics and offers activity suggestions to illustrate their use in counseling. Finally, the author offers best practices for implementing neuroeducation, including attention to counselor competence, client readiness, and neuroscience of learning principles. Implications for research are also discussed.
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Li, Di, i Xiangjian Chen. "Emotion Recognition Based on Type-2 Recurrent Wavelet Fuzzy Brain Emotion Learning Network Model". Mathematical Problems in Engineering 2021 (7.08.2021): 1–11. http://dx.doi.org/10.1155/2021/9991531.
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Emotion recognition plays a crucial role in human-robot emotional interaction applications, and the brain emotional learning model is one of several emotion recognition methods, but the learning rules of original brain emotional learning model play poor adaptation and do not work very well. In fact, existing facial emotion recognition methods do not have high accuracy and are not sufficiently practical in real-time applications. In order to solve this problem, this paper introduces an optimal model, which merges interval type-2 recurrent wavelet fuzzy system and brain emotional learning network for emotion recognition. The proposed model takes advantage of type-2 recurrent wavelet fuzzy theory and brain emotional neural network. There are no rules initially, and then the structure and parameters of model are tuning online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through a type-2 recurrent wavelet fuzzy inference system; then, the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. The proposed model could reduce the uncertainty in terms of vagueness by using type-2 recurrent wavelet fuzzy theory and removing noise samples. Finally, the superior performance of the proposed method is demonstrated by its comparison with some emotion recognition methods on five emotion databases.
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Naidu, D. J. Samatha, i G. Anand Kumar Reddy. "EARLIER DETECTION OF ALZHEIMER’S DISEASE USING IMAGE PROCESSING AND MACHINE LEARNING ALGORITHMS WITH GRAPH THEORY". International Journal of Computer Science and Mobile Computing 10, nr 8 (30.08.2021): 36–40. http://dx.doi.org/10.47760/ijcsmc.2021.v10i08.006.
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Alzheimer’s disease is one of the brain disease which is irreversible, progressive brain disorder that slowly destroys memory and thinking skills and, eventually, the ability to carry out the simplest tasks. There is no cure for Alzheimer’s disease but we prevent it’s by early detection. In existing work, limited with Alzheimer’s are irreversible, effect on daily activities, high memory loss and reducing the size of brain, etc. previous works focused on 2D and 3D formats now we considering 4D images. In proposed work, this work aims to present an automated method that assists in the diagnosis of Alzheimer’s disease supports the monitoring of the progression of the disease. The study of brain network based on resting-state functional Magnetic Resonance Imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize various aspects of the brain network by calculating measures the accuracy of different machine learning methods and different features to classify Cognitively Normal (C.N) individuals from Alzheimer’s Disease (A.D) and to predict longitudinal outcomes in participants with Mild Cognitive Impairment (MCI).
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WENG, JUYANG. "DEVELOPMENTAL ROBOTICS: THEORY AND EXPERIMENTS". International Journal of Humanoid Robotics 01, nr 02 (czerwiec 2004): 199–236. http://dx.doi.org/10.1142/s0219843604000149.
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A hand-designed internal representation of the world cannot deal with unknown or uncontrolled environments. Motivated by human cognitive and behavioral development, this paper presents a theory, an architecture, and some experimental results for developmental robotics. By a developmental robot, we mean that the robot generates its "brain" (or "central nervous system," including the information processor and controller) through online, real-time interactions with its environment (including humans). A new Self-Aware Self-Effecting (SASE) agent concept is proposed, based on our SAIL and Dav developmental robots. The manual and autonomous development paradigms are formulated along with a theory of representation suited for autonomous development. Unlike traditional robot learning, the tasks that a developmental robot ends up learning are unknown during the programming time so that the task-specific representation must be generated and updated through real-time "living" experiences. Experimental results with SAIL and Dav developmental robots are presented, including visual attention selection, autonomous navigation, developmental speech learning, range-based obstacle avoidance, and scaffolding through transfer and chaining.
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Hamdan AL-onizat, Sabah Hasan, i Yahya Hussain Othman AL-Qatawneh. "The Effectiveness of an Educational Program Built on the Brain-Based Learning Theory in Improving Mathematical Skills and Motivation for Learning among Student with Learning Disabilities in Jordan". Modern Applied Science 13, nr 11 (3.10.2019): 1. http://dx.doi.org/10.5539/mas.v13n11p1.
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This study aimed at investigating the effectiveness of an educational program built on the brain-based learning theory in improving the mathematical skills and motivation among students with learning disabilities. The sample of the study consisted of (60) student enrolled in learning disabilities’ recourses rooms from the third, fourth and fifth grades. The sample was divided randomly into two groups; an experimental group and a control group. In order to achieve the objectives of the study, the researchers have developed three achievement tests in Math for the third, fourth and fifth grades, mathematic motivation scale, and the psychometric properties of the scale in order to apply the pre-post-tests. The researchers also designed the educational program base on the brain-based learning theory. The implementation of the program took two consecutive months; (75) lessons, (2) lessons per day with a duration of (45) minutes for each lesson. After obtaining the results through the appropriate statistical analysis, the study concluded that there were statistically significant differences in the post-test of mathematical skills and its sub-dimensions in favour of the experimental group. There was no statistically significant effect for both gender and grade variables and the interaction between the educational program and grade on the achievement of mathematics skills. There were statistically significant differences on the post-test of motivation to learn mathematics and its sub-dimensions and in favour of the experimental group.
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Et. al., R. Aruna Kirithika,. "An Efficient ensemble of Brain Tumour Segmentation and Classification using Machine Learning and Deep Learning based Inception Networks". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, nr 2 (10.04.2021): 987–98. http://dx.doi.org/10.17762/turcomat.v12i2.1111.
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In recent times, Brain Tumor (BT) has become a common phenomenon affecting almost all age group of people. Identification of this deadly disease using computer tomography, magnetic resonance imaging are very popular now-a-days. Developing a Computer Aided Design (CAD) tool for diagnosis and classification of BT has become vital. This paper focuses on designing a tool for diagnosis and classification of BT using Deep Learning (DL) models, which involves a series of steps via acquiring (CT) image, pre-processing, segmenting and classifying to identify the type of tumor using SIFT with DL based Inception network model. The proposed model uses fuzzy C means algorithm for segmenting area of interest from the BT image acquired. Techniques like Gaussian Naïve Bayes (GNB) and logistic regression (LR) are used for classification processes. To ascertain all the techniques for its efficiency a benchmark dataset was used. The simulation outcome ensured that the performance of the proposed method with maximum sensitivity of 100%, specificity of 97.41% and accuracy of 97.96%.
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Mason, Jonathan. "From Learning to Consciousness: An Example Using Expected Float Entropy Minimisation". Entropy 21, nr 1 (13.01.2019): 60. http://dx.doi.org/10.3390/e21010060.
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Over recent decades several mathematical theories of consciousness have been put forward including Karl Friston’s Free Energy Principle and Giulio Tononi’s Integrated Information Theory. In this article we further investigate theory based on Expected Float Entropy (EFE) minimisation which has been around since 2012. EFE involves a version of Shannon Entropy parameterised by relationships. It turns out that, for systems with bias due to learning, certain choices for the relationship parameters are isolated since giving much lower EFE values than others and, hence, the system defines relationships. It is proposed that, in the context of all these relationships, a brain state acquires meaning in the form of the relational content of the associated experience. EFE minimisation is itself an association learning process and its effectiveness as such is tested in this article. The theory and results are consistent with the proposition of there being a close connection between association learning processes and the emergence of consciousness. Such a theory may explain how the brain defines the content of consciousness up to relationship isomorphism.
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Meeker, Mary. "Brain Research: the Necessity for Separating Sites, Actions and Functions". Gifted Education International 5, nr 3 (wrzesień 1988): 148–54. http://dx.doi.org/10.1177/026142948800500305.
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Some brain research concentrates solely on the study of sites and actions of brain functions but the writer argues that it is essential for research to investigate the functions also. Educators can offer measures of brain function such as the Structure of Intellect (S O I) and psychologists need information about the characteristics of brain functions if accurate diagnosis is to be made of learning abilities and disabilities. The writer stresses the importance of defining giftedness in far broader terms than the traditional psychometric measures of intelligence. She outlines the comprehensive range of brain functions incorporated by Guilford's theory of the Structure of Intellect and suggests that its most important use will be the partnership with brain research which itself needs a theory based test of brain functions.
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Et. al., Nisha Joseph,. "Study on Evaluation of Machine Learning Approaches in Brain Tumour MR Images". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, nr 5 (11.04.2021): 1361–71. http://dx.doi.org/10.17762/turcomat.v12i5.2028.
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The principal intention of this work is to compare the performance of the supervised brain tumour segmentation methods. These segmentation methods are based on machine learning. First, the input MR brain image is denoised by employing the adaptive bilateral filter, and the image contrast is enhanced employing the histogram equalization. Then we retrieve the features from the pre-processed image. Among several feature extraction methods, this work uses the shape, intensity, and texture feature extractors. Subsequent to removing these three types of features, fragment the tumor dependent on these recovered segments. The supervised segmentation approach is used for this. Among several supervised segmentation methods, this work uses three machine learning methods, namely Probabilistic Neural Network (PNN), Artificial Neural Network (ANN), and Convolution Neural Network (CNN). Finally, the retrieved features are feed into these machine learning methods to segment the brain tumour regions. To find out the best machine learning approach, the performance of these three supervised machines learning methods is evaluated by four performance metrics. Based on these evaluations, the best segmentation approach is discovered. Four execution boundaries are utilized, in particular, Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), Jaccard list (JI), and Sensitivity (SEN) to analyze the presentation of the AI strategy. The experimental outputs exposed that the CNN makes greater than other methods.
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PERLOVSKY, LEONID, i ROSS DEMING. "A MATHEMATICAL THEORY FOR LEARNING, AND ITS APPLICATION TO TIME-VARYING COMPUTED TOMOGRAPHY". New Mathematics and Natural Computation 01, nr 01 (marzec 2005): 147–71. http://dx.doi.org/10.1142/s1793005705000081.
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The brain has evolved to enable organisms to survive in a complicated and dynamic world. Its operation is based upon a priori models of the environment which are adapted, during learning, in response to new and changing stimuli. The same qualities that make biological learning mechanisms ideal for organisms make their underlying mathematical algorithms ideal for certain technological applications, especially those concerned with understanding the physical processes giving rise to complicated data sets. In this paper, we offer a mathematical model for the underlying mechanisms of biological learning, and we show how this mathematical approach to learning can yield a solution to the problem of imaging time-varying objects from X-ray computed tomographic (CT) data. This problem relates to several practical aspects of CT imaging including the correction of motion artifacts caused by patient movement or breathing.
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Sulaila, Neneng. "آراء النظرية البنائية في تعلّم اللغة الثانية والاستفادة منها في تعليم اللغة العربية لغير الناطقين بها". Rayah Al-Islam 2, nr 01 (28.04.2018): 12–22. http://dx.doi.org/10.37274/rais.v2i01.28.
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This study used a descriptive analytical approach to constructivism theory in learning a second language and used it in teaching Arabic to non-Arabic speakers by making literature study as a source of data collection. The results of the study showed that (a) The theory of constructivism was the philosophy of education which said that students develop their own knowledge that he keeps in his mind, so learning is an ongoing process. (b) Based on the principle of internal mental processes that occur in the student's brain so that they link past knowledge and present knowledge with the construction of learning activities that produce meaning. (c) The process of learning Arabic occurs after the information is constructed from the process of listening, speaking or writing in accordance with the rules of Arabic, and incorporating the knowledge gained from past knowledge that is influenced by the environment. (d) Learning in constructivism theory is based on four phases: (1) Advocacy (2) Exploration (3) Proposing interpretations and solutions (4) Taking action. (e) Applying constructivism theory in teaching Arabic to non-Arabic speakers based on the principles established by active students in constructing their linguistic knowledge.
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Lin, Ya-Wei, i Oleg Bazaluk. "Using Brain Science Theory to Analyze the Unity between Language Input and Output: Methodology Improvement Substantiation". PSYCHOLINGUISTICS 27, nr 1 (16.04.2020): 195–218. http://dx.doi.org/10.31470/2309-1797-2020-27-1-195-218.
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Introduction. Based on the brain science theory of “how people learn” and in order to modernize the methodology of psycholinguistic research, this research used documentary analysis and addressed the standpoint that the 4MAT Teaching and Learning Model can be subsumed into or superimposed on the Kirkpatrick Four-Level Evaluation Model, and vice versa. Meanwhile, the phase of language input and output is analyzed on the basis of the two Models above. In the end, some implications arise so as to provide reference for prospective researchers and practitioners in psycholinguistics.
The aim of the study. The 4MAT Teaching and Learning Model and the Kirkpatrick Four-Level Evaluation Model are both widely applied, so a deliberate literature review to clarify the integration and the unity between them is conducted that expects to make some theoretical references inspired by the unity available to a wide range of linguistic teaching design and learning performance evaluation.
The authors argue that the references interconnect teaching design and learning performance evaluation in light of language input and output and therefore help linguistic teachers/trainers with a whole and valid scheme at the very beginning of student learning, and this is the unity that also corresponds to Kirkpatrick & Kirkpatrick’s standpoint: “The end is the beginning”.
Research methods. The study was conducted using the semantic differential scaling and the method of documentary analysis.
Results. A combination of brain science theory and Fractal Information Theory has verified initially how the 4MAT Teaching and Learning Model and the Kirkpatrick Four-Level Evaluation Model subsume and superimpose in terms of the theoretical framework, i.e., the unity between a teaching theory and a learning performance evaluation theory. Such integration not only originates from the inherent unity verified by a thoughtful literature review but also receives theoretical support from interdisciplinary studies. Meanwhile, this integration is intertwined with language input and output in a psycholinguistic/neurolinguistic manner.
Conclusions. A primary investigation using brain science theory and other theories to analyze the integration between the 4MAT Teaching and Learning Model and the Kirkpatrick Four-Level Evaluation Model shows the unity between both models. This investigation led to achieving the purpose of the study: modernizing the methodology of psycholinguistic research. With implementing the components/stages of language input and output as this article proposed, it is expected to be promising in extending and applying both models theoretically and practically in linguistics and other relevant areas in the future. As it comes to studies, it is recommended that the two Models be connected to analyze more teaching models and/or learning performance evaluation models for unity, inquire performance evaluation in collaborations with scenarios in practice, or even associate other disciplines under the implementation of Fractal Information Theory. A possible suggestion for psycholinguistic researchers is to design curricular and lessons based on the Unified Models (Figure 1 & 2) proposed in this study and evaluate instructional efficacy and student learning performance. Another potential research direction is to use each quadrant of the Unified Models and analyze related components in more specific language input and output phases: listening, reading, speaking, writing, and even smaller components in the four types of language skills. As it comes to practice, especially in psycholinguistics and/or other relevant disciplines, the key to apply the two target Models simultaneously depends on how to regulate respective quadrants/levels pro rata as well as the wholeness between them to simultaneously achieve “dynamic equilibrium” in the 4MAT Teaching and Learning Model and “The end is the beginning” in the Kirkpatrick Four-Level Evaluation Model.
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Sousa, Regina, Tiago Lima, António Abelha i José Machado. "Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting". Electronics 10, nr 14 (8.07.2021): 1630. http://dx.doi.org/10.3390/electronics10141630.
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Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to recognize audio, image, and time series predictions, has helped to solve various types of problems. This paper aims to introduce a new theory, Hierarchical Temporal Memory (HTM), that applies to stock market prediction. HTM is based on the biological functions of the brain as well as its learning mechanism. The results are of significant relevance and show a low percentage of errors in the predictions made over time. It can be noted that the learning curve of the algorithm is fast, identifying trends in the stock market for all seven data universes using the same network. Although the algorithm suffered at the time a pandemic was declared, it was able to adapt and return to good predictions. HTM proved to be a good continuous learning method for predicting time series datasets.
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Misra, Joyneel, Srinivas Govinda Surampudi, Manasij Venkatesh, Chirag Limbachia, Joseph Jaja i Luiz Pessoa. "Learning brain dynamics for decoding and predicting individual differences". PLOS Computational Biology 17, nr 9 (3.09.2021): e1008943. http://dx.doi.org/10.1371/journal.pcbi.1008943.
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Insights from functional Magnetic Resonance Imaging (fMRI), as well as recordings of large numbers of neurons, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of brain signals. To decode brain dynamics, we propose an architecture based on recurrent neural networks to uncover distributed spatiotemporal signatures. We demonstrate the potential of the approach using human fMRI data during movie-watching data and a continuous experimental paradigm. The model was able to learn spatiotemporal patterns that supported 15-way movie-clip classification (∼90%) at the level of brain regions, and binary classification of experimental conditions (∼60%) at the level of voxels. The model was also able to learn individual differences in measures of fluid intelligence and verbal IQ at levels comparable to that of existing techniques. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational (i.e., classification related) properties of brain dynamics. Finally, saliency maps and lesion analysis were employed to characterize brain-region/voxel importance, and uncovered how dynamic but consistent changes in fMRI activation influenced decoding performance. When applied at the level of voxels, our framework implements a dynamic version of multivariate pattern analysis. Our approach provides a framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.
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Luo, Dali. "Guide Teaching System Based on Artificial Intelligence". International Journal of Emerging Technologies in Learning (iJET) 13, nr 08 (30.08.2018): 90. http://dx.doi.org/10.3991/ijet.v13i08.9058.
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To improve the development and deployment efficiency of the system, this paper combined the software system with Java and AI language Prolog to achieve the guide teaching system based on artificial intel-ligence (AI). The system creatively adopted the theory of artificial intelligence expert system, at the same time, built a Struts+Spring+Hibernate lightweight JavaEE framework. The coupling degree of each module in the system was greatly reduced to facilitate the expansion of future functions. Based on the development principle of the artificial intelligence expert system, the system diagnosed the learner's mastery of each point of knowledge. It classified students' learning effect and evaluated the knowledge points. Making full use of the learning state of students and combining it with artificial intelligence expert system theory, the system developed a suitable learning strategy to help students improve their learning with less efforts. In addition, the system took the forgetting rule of human brain into account, which periodically presented trainees’ knowledge points assessment and avoided students wasting time. The purpose was to help students improve their learning effect. Finally, the system was tested. The test results showed that the system is applicable and useful. It is concluded that the artificial intelligence system provides an example for the same method and has certain reference significance.
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Kim, Yong-Seong, Hag-Jun Lee, Jae-Bun Oh i Jeong-Ha Yoon. "An Exploration of Principle of Representation and Instruction Strategies in Universal Design for Learning based on Brain Science Theory". Journal of special education : theory and practice 20, nr 2 (30.06.2019): 391–425. http://dx.doi.org/10.19049/jsped.2019.20.2.16.
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Sato, João Ricardo, Claudinei Eduardo Biazoli, Giovanni Abrahão Salum, Ary Gadelha, Nicolas Crossley, Gilson Vieira, André Zugman i in. "Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning". World Journal of Biological Psychiatry 19, nr 2 (8.02.2017): 119–29. http://dx.doi.org/10.1080/15622975.2016.1274050.
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van de Water, Manon. "Drama in education: why drama is necessary". SHS Web of Conferences 98 (2021): 02009. http://dx.doi.org/10.1051/shsconf/20219802009.
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The article dwells on the use of drama and performance techniques in education and social work in connection with multiple intelligence theory, emotional intelligence theory, and brain based learning. The author connects the use of drama in the alternative theories of teaching and learning based on recent neuroscientific research, and lays out an integrative approach to teaching and learning that promotes inclusion, diversity, and social awareness, through embodied and contextualized learning. If we perceive cognition and emotion as interrelated, then drama as an educational tool becomes essential. It creates metaphors of our lives, which we lead through both cognitive and emotional domains. Art and creativity play an essential role in connections between the body, emotions, and the mind. Moreover, as we live in relationship to the rest of the world around us, our learning is embodied, our brain, emotions, and physiology are constantly connected. Thus, the article demonstrates that drama and performance are vital in teaching the whole child, whether taught as a discipline or used as a teaching tool. This means, the author claims, educators, neuropsychologists, and theatre and drama specialists have to have open minds and be willing to step out of comfort zones and together make a case for using theatre and drama methods as a way to improve human lives.
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Dong, He, i Sang-Bing Tsai. "An Empirical Study on Application of Machine Learning and Neural Network in English Learning". Mathematical Problems in Engineering 2021 (22.07.2021): 1–9. http://dx.doi.org/10.1155/2021/8444858.
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With the continuous development of neural network theory itself and related theories and related technologies, neural network is one of the main branches of intelligent control technology. Artificial neural network is a nonlinear and adaptive information processing composed of a large number of processing units. In this paper, an adaptive fuzzy neural network (FNN) is used to construct an intelligent system architecture for English learning, and activation function is used to apply the knowledge of computer science and linguistics to English learning. The network neural structure diagram is presented. English machine learning model framework is established based on recursive neural network. On this basis, feature vector extraction and normalization algorithm are used to meet the needs of neural network model. After acquiring the feature vectors of users’ learning styles, the clustering algorithm is used to effectively form a variety of learning styles. The validity of the English learning model was verified by designing the functional flow based on tests. Accurate mastery can activate the corresponding brain regions not only to improve the efficiency of learning, but also to better facilitate language learning.
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Górriz, Juan M., Javier Ramírez, F. Segovia, Francisco J. Martínez, Meng-Chuan Lai, Michael V. Lombardo, Simon Baron-Cohen i John Suckling. "A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms". International Journal of Neural Systems 29, nr 07 (6.08.2019): 1850058. http://dx.doi.org/10.1142/s0129065718500582.
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Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above [Formula: see text] on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism ([Formula: see text], [Formula: see text]/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.
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Khacef, Lyes, Laurent Rodriguez i Benoît Miramond. "Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning". Electronics 9, nr 10 (1.10.2020): 1605. http://dx.doi.org/10.3390/electronics9101605.
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Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence). Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a Dynamic Vision Sensor (DVS)/EletroMyoGraphy (EMG) hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system’s topology is not fixed by the user but learned along the system’s experience through self-organization.
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Solovieva, Yulia, i Luis Quintanar. "Psychological Concepts of Activity Theory in Child Neuropsychology". Journal of Education and Culture Studies 1, nr 1 (5.04.2017): 25. http://dx.doi.org/10.22158/jecs.v1n1p25.
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<p><em>Neuropsychology is the part of psychology, which studies the relation between psychological and brain level of organization of human activity. It is possible to identify specific mechanisms or components of psychological processes related to the functioning of special brain zones. Such a study can be based on different general psychological theories. From the point of view of activity theory approach these components can be understood as psycho-physiological structural and systemic mechanisms of actions and operations fulfilled by a subject in the context of one or another general activity. In other words, neuropsychological level of analyses could be understood as the elementary level of human activity. Neuropsychological analysis can be organized as assessment of actions and not of isolated functions. The present study shows how functional disorders of psychophysiological mechanisms can affect the fulfillment of the same actions of children with learning disabilities. The discussion stresses the importance of inclusion of the terms of activity theory to the practice in neuropsychology.</em></p>
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Han, Kun, Dewei Wu i Lei Lai. "A Brain-Inspired Adaptive Space Representation Model Based on Grid Cells and Place Cells". Computational Intelligence and Neuroscience 2020 (11.08.2020): 1–12. http://dx.doi.org/10.1155/2020/1492429.
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Grid cells and place cells are important neurons in the animal brain. The information transmission between them provides the basis for the spatial representation and navigation of animals and also provides reference for the research on the autonomous navigation mechanism of intelligent agents. Grid cells are important information source of place cells. The supervised learning and unsupervised learning models can be used to simulate the generation of place cells from grid cell inputs. However, the existing models preset the firing characteristics of grid cell. In this paper, we propose a united generation model of grid cells and place cells. First, the visual place cells with nonuniform distribution generate the visual grid cells with regional firing field through feedforward network. Second, the visual grid cells and the self-motion information generate the united grid cells whose firing fields extend to the whole space through genetic algorithm. Finally, the visual place cells and the united grid cells generate the united place cells with uniform distribution through supervised fuzzy adaptive resonance theory (ART) network. Simulation results show that this model has stronger environmental adaptability and can provide reference for the research on spatial representation model and brain-inspired navigation mechanism of intelligent agents under the condition of nonuniform environmental information.
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Mastin, Marla. "Storytelling + Origami = Storigami Mathematics". Teaching Children Mathematics 14, nr 4 (listopad 2007): 206–12. http://dx.doi.org/10.5951/tcm.14.4.0206.
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All educators continually search for ways to assist students in learning mathematical concepts. The challenge for teachers is to provide a “thinking” curriculum and creative instructional methods while helping students recognize that they should be actively involved in their own learning. This article presents a way to engage students in mathematics through the use of an innovative instructional method based on constructivist theory, which emphasizes the “building” that takes place in the brain as a person learns and which is rooted in both the social and the cognitive perspective of learning.
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Zhang, Zhewei, Huzi Cheng i Tianming Yang. "A recurrent neural network framework for flexible and adaptive decision making based on sequence learning". PLOS Computational Biology 16, nr 11 (3.11.2020): e1008342. http://dx.doi.org/10.1371/journal.pcbi.1008342.
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The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism’s survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. This is analogous to the statistical inference that has been extensively studied in the natural language processing field, where recent developments of recurrent neural networks have found many successes. We wonder whether these neural networks, the gated recurrent unit (GRU) networks in particular, reflect how the brain solves the contingency problem. Therefore, we build a GRU network framework inspired by the statistical learning approach of NLP and test it with four exemplar behavior tasks previously used in empirical studies. The network models are trained to predict future events based on past events, both comprising sensory, action, and reward events. We show the networks can successfully reproduce animal and human behavior. The networks generalize the training, perform Bayesian inference in novel conditions, and adapt their choices when event contingencies vary. Importantly, units in the network encode task variables and exhibit activity patterns that match previous neurophysiology findings. Our results suggest that the neural network approach based on statistical sequence learning may reflect the brain’s computational principle underlying flexible and adaptive behaviors and serve as a useful approach to understand the brain.
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Li, Hongmei, Di Li, Xiangjian Chen i Zhongqiang Pan. "Data-Driven Control Based on the Interval Type-2 Intuition Fuzzy Brain Emotional Learning Network for the Multiple Degree-of-Freedom Rehabilitation Robot". Mathematical Problems in Engineering 2021 (15.01.2021): 1–15. http://dx.doi.org/10.1155/2021/8892290.
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A novel interval type-2 intuition fuzzy brain emotional learning network model (IT2IFBELC) which depends only on the input and output data is proposed for the rehabilitation robot, which is different from model-based control algorithms that require exact dynamic model knowledge of the rehabilitation robot. The proposed model takes advantage of the type-2 intuition fuzzy theory and brain emotional neural network, and this is no rule initially; then, the structure and parameters of IT2IFBELC are tuned online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through an interval type-2 intuition fuzzy inference system (IT2IFIS), and then the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the whole controller is composed of three parts, including the ideal sliding mode controller, the interval type-2 intuition fuzzy brain emotional learning network controller, and a powerful robust compensation controller, and then one Lyapunov function is designed to guarantee the rapid convergence of the control systems. For further illustrating the superiority of this model, several models are studied here for comparison, and the results show that the interval type-2 intuition fuzzy brain emotional learning network model can obtain better satisfactory control performance and be suitable to deal with the influence of the uncertainty of the rehabilitation robot.
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Majidov, Ikhtiyor, i Taegkeun Whangbo. "Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods". Sensors 19, nr 7 (11.04.2019): 1736. http://dx.doi.org/10.3390/s19071736.
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Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain–computer interface applications. In the field of information theory, Riemannian geometry is mainly used with covariance matrices. Accordingly, investigations showed that if the method is used after the execution of the filterbank approach, the covariance matrix preserves the frequency and spatial information of the signal. Deep-learning methods are superior when the data availability is abundant and while there is a large number of features. The purpose of this study is to a) show how to use a single deep-learning-based classifier in conjunction with BCI (brain–computer interface) applications with the CSP (common spatial features) and the Riemannian geometry feature extraction methods in BCI applications and to b) describe one of the wrapper feature-selection algorithms, referred to as the particle swarm optimization, in combination with a decision tree algorithm. In this work, the CSP method was used for a multiclass case by using only one classifier. Additionally, a combination of power spectrum density features with covariance matrices mapped onto the tangent space of a Riemannian manifold was used. Furthermore, the particle swarm optimization method was implied to ease the training by penalizing bad features, and the moving windows method was used for augmentation. After empirical study, the convolutional neural network was adopted to classify the pre-processed data. Our proposed method improved the classification accuracy for several subjects that comprised the well-known BCI competition IV 2a dataset.
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Drugowitsch, Jan, André G. Mendonça, Zachary F. Mainen i Alexandre Pouget. "Learning optimal decisions with confidence". Proceedings of the National Academy of Sciences 116, nr 49 (15.11.2019): 24872–80. http://dx.doi.org/10.1073/pnas.1906787116.
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Diffusion decision models (DDMs) are immensely successful models for decision making under uncertainty and time pressure. In the context of perceptual decision making, these models typically start with two input units, organized in a neuron–antineuron pair. In contrast, in the brain, sensory inputs are encoded through the activity of large neuronal populations. Moreover, while DDMs are wired by hand, the nervous system must learn the weights of the network through trial and error. There is currently no normative theory of learning in DDMs and therefore no theory of how decision makers could learn to make optimal decisions in this context. Here, we derive such a rule for learning a near-optimal linear combination of DDM inputs based on trial-by-trial feedback. The rule is Bayesian in the sense that it learns not only the mean of the weights but also the uncertainty around this mean in the form of a covariance matrix. In this rule, the rate of learning is proportional (respectively, inversely proportional) to confidence for incorrect (respectively, correct) decisions. Furthermore, we show that, in volatile environments, the rule predicts a bias toward repeating the same choice after correct decisions, with a bias strength that is modulated by the previous choice’s difficulty. Finally, we extend our learning rule to cases for which one of the choices is more likely a priori, which provides insights into how such biases modulate the mechanisms leading to optimal decisions in diffusion models.
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Backman, Ylva, Viktor Gardelli i Peter Parnes. "Game Technologies to Assist Learning of Communication Skills in Dialogic Settings for Persons with Aphasia". International Journal of Emerging Technologies in Learning (iJET) 16, nr 03 (12.02.2021): 190. http://dx.doi.org/10.3991/ijet.v16i03.17889.
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Persons with aphasia suffer from a loss of communication ability as a consequence of a brain injury. While rare, a small strand of research indicates effectiveness of dialogic interventions for communication development for persons with aphasia, but a vast amount of research studies shows its effectiveness for other target groups. In this paper, we describe the main parts of the hitherto technological development of an application named Dialogica that is (i) aimed at facilitating increased communicative participation in dialogic settings for persons with aphasia and other communication disorders, (ii) based on computer game technology as well as on theory in dialogic education and argumentation theory, and (iii) designed for mobile devices with larger screens.
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Et. al., Nur Nabilah Abu Mangshor,. "Analysison Students’ Learning Habits: Identifyingthe Contributory Factorsof Learning Duringthe Covid-19 PandemicUsing Radial Basis Function (RBF)". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, nr 5 (10.04.2021): 1736–43. http://dx.doi.org/10.17762/turcomat.v12i5.2171.
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: The Artificial Neural Network (ANN) is an Artificial Intelligence technique that offer many benefits including the ability to process a vast amount of data, the ability to learn from experiences, and the good generalization capability. It was invented based on the concept of imitation of the human brain and built up of nodes that are like human neurons. The Radial Basis Function (RBF) is one of the established types of ANN. Considering the advantages and great performance of the RBF, this study aims to investigate the contributory factors of students’ learning habits during the Coronavirus Disease 2019 (or known as COVID-19) pandemic using RBF. Responses from a total of 420 respondents were collected from Vietnamese students’ learning habits during the COVID-19 pandemic dataset that was established from the questionnaires distributed in the period of 7th February 2020 to 28th February 2020. Fifteen independent variables were used as the input for the RBF network which is based on the 15-9-1 structure. Based on the experiment conducted, the implementation of the RBF model was found to be fair and effective with the small number of Sum of Square Error (SSE) and Relative Error (RE) produced. It could also be concluded that the most contributing factor of students’ learning habits during the COVID-19 pandemic is the learning hours per day for self-learning before the pandemic.
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Agustina, Widya, i Lilies Youlia Friatin. "STUDENTS’ VOICE: APPLYING BRAIN-WRITING IN WRITING RECOUNT TEXT". Jurnal Wahana Pendidikan 6, nr 2 (6.12.2019): 9. http://dx.doi.org/10.25157/wa.v6i2.2967.
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This study was aimed at finding out thestudents’ perceptions about the implementation of brain-writing in teaching writing recount text.In order to support the data analysis, the writers used the theory from Sadker and Ellen (2007, p. 6)about the steps in implementing brain writing technique in teaching recount text. Furthermore, in conducting this study the writers used qualitative research in which case study was employed to collect the data from participants in this research that was English teacher who taught recount text through brain-writing technique. Moreover, interview was the instrument used by the writers in collecting the data, then the data analyzed qualitatively. Based on the research findingsthe writersconcluded that the teacher did some steps in implementing brain-writing including. Overall, the steps in implementing brain-writing was relevant with the theory from Sadker, Ellen (2007, p. 6).The second conclusion was about student’s perception in implementing brain-writing technique in teaching writing recount text, the writers concluded that the students viewed the teaching-learning process of recount text through brain-writing technique was enjoy activities that not only improve students’ motivation and students’ achievement.Moreover, the writers suggests that the further researcher to investigate students’ difficulties on the use brain-writing technique in teaching writing
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Joo, Woong-young. "The Characteristics of History lesson in the Elementary social studies classroom -An approach in perspective of a Brain-based learning theory-". Society of History Education 58 (28.02.2016): 21–58. http://dx.doi.org/10.17999/sohe.2016.58.02.
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Liu, Huaping, Di Guo, Fuchun Sun, Wuqiang Yang, Steve Furber i Tengchen Sun. "Embodied tactile perception and learning". Brain Science Advances 6, nr 2 (czerwiec 2020): 132–58. http://dx.doi.org/10.26599/bsa.2020.9050012.
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Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.
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Qiao, Chen, Lujia Lu, Lan Yang i Paul J. Kennedy. "Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology". Applied Sciences 9, nr 10 (26.05.2019): 2148. http://dx.doi.org/10.3390/app9102148.
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Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial for diagnosis and further precision medicine. In this paper, we propose a hybrid feature selection method based on machine learning and traditional statistical approaches and explore the brain abnormalities of schizophrenia by using the functional and structural MRI data. The results show that the abnormal brain regions are mainly distributed in the supramarginal gyrus, cingulate gyrus, frontal gyrus, precuneus and caudate, and the abnormal functional connections are related to the caudate nucleus, insula and rolandic operculum. In addition, some complex network analyses based on graph theory are utilized on the functional connection data, and the results demonstrate that the located abnormal functional connections in brain can distinguish schizophrenia patients from healthy controls. The identified abnormalities in brain with schizophrenia by the proposed hybrid feature selection method show that there do exist some abnormal brain regions and abnormal disruption of the network segregation and network integration for schizophrenia, and these changes may lead to inaccurate and inefficient information processing and synthesis in the brain, which provide further evidence for the cognitive dysmetria of schizophrenia.
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Prakash, Bhargav, Gautam Kumar Baboo i Veeky Baths. "A Novel Approach to Learning Models on EEG Data Using Graph Theory Features—A Comparative Study". Big Data and Cognitive Computing 5, nr 3 (28.08.2021): 39. http://dx.doi.org/10.3390/bdcc5030039.
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Brain connectivity is studied as a functionally connected network using statistical methods such as measuring correlation or covariance. The non-invasive neuroimaging techniques such as Electroencephalography (EEG) signals are converted to networks by transforming the signals into a Correlation Matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regression, Random Forest, Support Vector Machine, and Recurrent Neural Networks (RNN), are implemented on two different types of correlation matrices: Correlation Matrix (static connectivity) and Time-resolved Correlation Matrix (dynamic connectivity), to classify them either on their psychometric assessment or the effect of therapy. These correlation matrices are different from traditional learning techniques in the sense that they incorporate theory-based graph features into the learning models, thus providing novelty to this study. The EEG data used in this study is trail-based/event-related from five different experimental paradigms, of which can be broadly classified as working memory tasks and assessment of emotional states (depression, anxiety, and stress). The classifications based on RNN provided higher accuracy (74–88%) than the other three models (50–78%). Instead of using individual graph features, a Correlation Matrix provides an initial test of the data. When compared with the Time-resolved Correlation Matrix, it offered a 4–5% higher accuracy. The Time-resolved Correlation Matrix is better suited for dynamic studies here; it provides lower accuracy when compared to the Correlation Matrix, a static feature.
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Ownsworth, Tamara. "To Err is Human; to Self-Regulate After Brain Injury, Divine". Brain Impairment 16, nr 3 (25.11.2015): 236–42. http://dx.doi.org/10.1017/brimp.2015.26.
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Traumatic brain injury (TBI) can reduce people's ability to monitor their own actions and identify and correct errors on everyday tasks. This usually occurs because of damage to neural pathways that support ‘metacognition’ or the higher-order capacity to reflect upon and regulate one's own behaviour. This paper initially reviews the neuro-cognitive mechanisms underlying error self-regulation. An overview of assessment approaches is provided which emphasises how approaches to measuring error self-regulation following TBI have been extended from the laboratory to people's real life environments. Over the last few decades, the evidence base supporting the efficacy of error-based learning or metacognitive approaches in rehabilitation has advanced considerably. An overview of the theory underpinning rehabilitation approaches and evidence supporting the efficacy of error-based learning is provided. Finally, the paper briefly describes the protocol for a randomised controlled trial that aims to determine whether people with severe TBI benefit from making errors when they learn new complex tasks.
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Field, Duncan, Yanis Ammouche, José-Maria Peña i Antoine Jérusalem. "Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter". Computational Mechanics 67, nr 6 (28.04.2021): 1629–43. http://dx.doi.org/10.1007/s00466-021-02009-1.
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AbstractA modular pipeline for improving the constitutive modelling of composite materials is proposed.The method is leveraged here for the development of subject-specific spatially-varying brain white matter mechanical properties. For this application, white matter microstructural information is extracted from diffusion magnetic resonance imaging (dMRI) scans, and used to generate hundreds of representative volume elements (RVEs) with randomly distributed fibre properties. By automatically running finite element analyses on these RVEs, stress-strain curves corresponding to multiple RVE-specific loading cases are produced. A mesoscopic constitutive model homogenising the RVEs’ behaviour is then calibrated for each RVE, producing a library of calibrated parameters against each set of RVE microstructural characteristics. Finally, a machine learning layer is implemented to predict the constitutive model parameters directly from any new microstructure. The results show that the methodology can predict calibrated mesoscopic material properties with high accuracy. More generally, the overall framework allows for the efficient simulation of the spatially-varying mechanical behaviour of composite materials when experimentally measured location-specific fibre geometrical characteristics are provided.
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Kawakubo, Hideko, Yusuke Matsui, Itaru Kushima, Norio Ozaki i Teppei Shimamura. "A network of networks approach for modeling interconnected brain tissue-specific networks". Bioinformatics 35, nr 17 (15.01.2019): 3092–101. http://dx.doi.org/10.1093/bioinformatics/btz032.
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Abstract Motivation Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits and regions which are connected each other and affect behavior and cognitive processes. Results We introduce a novel and efficient framework, called a ‘Network of Networks’ approach, to infer the interconnectivity structure between multiple networks where the response and the predictor variables are topological information matrices of given networks. We also propose Graph-Oriented SParsE Learning, a new sparse structural learning algorithm for network data to identify a subset of the topological information matrices of the predictors related to the response. We demonstrate on simulated data that propose Graph-Oriented SParsE Learning outperforms existing kernel-based algorithms in terms of F-measure. On real data from human brain region-specific functional networks associated with the autism risk genes, we show that the ‘Network of Networks’ model provides insights on the autism-associated interconnectivity structure between functional interaction networks and a comprehensive understanding of the genetic basis of autism across diverse regions of the brain. Availability and implementation Our software is available from https://github.com/infinite-point/GOSPEL. Supplementary information Supplementary data are available at Bioinformatics online.
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Moldwin, Toviah, Odelia Schwartz i Elyse S. Sussman. "Statistical Learning of Melodic Patterns Influences the Brain's Response to Wrong Notes". Journal of Cognitive Neuroscience 29, nr 12 (grudzień 2017): 2114–22. http://dx.doi.org/10.1162/jocn_a_01181.
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The theory of statistical learning has been influential in providing a framework for how humans learn to segment patterns of regularities from continuous sensory inputs, such as speech and music. This form of learning is based on statistical cues and is thought to underlie the ability to learn to segment patterns of regularities from continuous sensory inputs, such as the transition probabilities in speech and music. However, the connection between statistical learning and brain measurements is not well understood. Here we focus on ERPs in the context of tone sequences that contain statistically cohesive melodic patterns. We hypothesized that implicit learning of statistical regularities would influence what was held in auditory working memory. We predicted that a wrong note occurring within a cohesive pattern (within-pattern deviant) would lead to a significantly larger brain signal than a wrong note occurring between cohesive patterns (between-pattern deviant), even though both deviant types were equally likely to occur with respect to the global tone sequence. We discuss this prediction within a simple Markov model framework that learns the transition probability regularities within the tone sequence. Results show that signal strength was stronger when cohesive patterns were violated and demonstrate that the transitional probability of the sequence influences the memory basis for melodic patterns. Our results thus characterize how informational units are stored in auditory memory trace for deviance detection and provide new evidence about how the brain organizes sequential sound input that is useful for perception.
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Mathias, Brian, Leona Sureth, Gesa Hartwigsen, Manuela Macedonia, Katja M. Mayer i Katharina von Kriegstein. "Visual Sensory Cortices Causally Contribute to Auditory Word Recognition Following Sensorimotor-Enriched Vocabulary Training". Cerebral Cortex 31, nr 1 (22.09.2020): 513–28. http://dx.doi.org/10.1093/cercor/bhaa240.
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Abstract Despite a rise in the use of “learning by doing” pedagogical methods in praxis, little is known as to how the brain benefits from these methods. Learning by doing strategies that utilize complementary information (“enrichment”) such as gestures have been shown to optimize learning outcomes in several domains including foreign language (L2) training. Here we tested the hypothesis that behavioral benefits of gesture-based enrichment are critically supported by integrity of the biological motion visual cortices (bmSTS). Prior functional neuroimaging work has implicated the visual motion cortices in L2 translation following sensorimotor-enriched training; the current study is the first to investigate the causal relevance of these structures in learning by doing contexts. Using neuronavigated transcranial magnetic stimulation and a gesture-enriched L2 vocabulary learning paradigm, we found that the bmSTS causally contributed to behavioral benefits of gesture-enriched learning. Visual motion cortex integrity benefitted both short- and long-term learning outcomes, as well as the learning of concrete and abstract words. These results adjudicate between opposing predictions of two neuroscientific learning theories: While reactivation-based theories predict no functional role of specialized sensory cortices in vocabulary learning outcomes, the current study supports the predictive coding theory view that these cortices precipitate sensorimotor-based learning benefits.
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Cazé, Romain, Mehdi Khamassi, Lise Aubin i Benoît Girard. "Hippocampal replays under the scrutiny of reinforcement learning models". Journal of Neurophysiology 120, nr 6 (1.12.2018): 2877–96. http://dx.doi.org/10.1152/jn.00145.2018.
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Multiple in vivo studies have shown that place cells from the hippocampus replay previously experienced trajectories. These replays are commonly considered to mainly reflect memory consolidation processes. Some data, however, have highlighted a functional link between replays and reinforcement learning (RL). This theory, extensively used in machine learning, has introduced efficient algorithms and can explain various behavioral and physiological measures from different brain regions. RL algorithms could constitute a mechanistic description of replays and explain how replays can reduce the number of iterations required to explore the environment during learning. We review the main findings concerning the different hippocampal replay types and the possible associated RL models (either model-based, model-free, or hybrid model types). We conclude by tying these frameworks together. We illustrate the link between data and RL through a series of model simulations. This review, at the frontier between informatics and biology, paves the way for future work on replays.