Download or read book Functional Characteristics of Neural Networks in Human Associative Learning written by Zainab Fatima and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning and memory are highly complex and adaptive cognitive processes of the human brain. The present body of work examined functional characteristics of neural networks that supported associative learning in multiple dimensions (e.g. space, time, frequency) and related them to cognitive markers and behavioral performance. Research was conducted with magnetoencephalography (MEG). Given the multidimensional nature of MEG data, several methodological tools were developed concurrently to enable better identification of learning-induced changes in the brain. Study 1 systematically examined the impact of different artifacts on scalp signals. Combined with simulations, it was established that detection of deep/weak sources (e.g. medial temporal lobes, striatum etc.) was substantially improved by removing noise from artifacts. This work culminated in the development of an artifact correction tool that is freely available for reseach use. Study 2 used artifact-free data from Study 1 to examine relationships between time-dependent changes in functional network organization, cognitive skills and behavioral performance. Results showed that individual variations in learning were supported by differences in cognitive ability and time-sensitive connectivity in functional networks. This research has translational scope for customizing rehabilitative practices based on an individualâ s learner profile. Study 3 examined frequency-specific changes in functional network interactions and their implications for adaptive control of behavior during learning. First, an automated data-driven pipeline was developed to obtain distributed brain sources. Phase synchronization was used to measure changes in network interactions. Reorganization of functional networks was related to distinct behavior types â eye movements and error rate. Results highlighted a shift in theta frequencies from early to late periods of learning. Both behavioral measures were shaped by similar network configurations early in learning and dissociable functional networks late in learning. The direct comparison made between implicit and explicit behaviors in this study has practical implications for research in special populations (e.g. development, disease) whereby implicit measures may be the only option for evaluating integrity of brain function.
Download or read book Associative Learning in Cortical and Artificial Neural Networks written by Chi Zhang and published by . This book was released on 2020 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: "One of the main goals of neuroscience is to explain the basic functions of the brain such as thought, learning, and control of movement. A comprehensive explanation of these functions must span different temporal and spatial scales to connect the workings of the brain at the molecular level to the circuit level to the level of behavior. This dissertation focuses on learning and formation of long-term memories - functions that are mediated by changes in synaptic connectivity. I examine the effects of learning on the connectivity and dynamics of networks in the brain and artificial neural networks. In the first chapter of this dissertation, I propose that many basic structural and dynamical properties of local cortical circuits result from associative learning. This hypothesis is tested in a network model of inhibitory and excitatory McCulloch and Pitts neurons loaded with associative sequences to capacity. I solve the learning problem analytically and numerically to show that such networks exhibit many ubiquities properties of local cortical citrus. These include structural properties, such as the probabilities of connections between inhibitory and excitatory neurons, distributions of weights for these connection types, overexpression of specific 2- and 3-neuron motifs, along with various properties of network dynamics. Because signal transmission in the brain is accompanied by many sources of errors and noise, in the second chapter of this dissertation I explore the effect of such unavoidable hindrances on learning and network properties. I argue that noise should not be viewed as a nuisance, but that it is an essential component of the reliable learning mechanism implemented by the brain. To test this hypothesis, I formulate and solve a biologically constrained network model of associative sequence learning in the presence of errors and noise. The results reveal that noise during learning increases the probability of memory retrieval and that it is required for optimal recovery of stored information. In the last chapter, I transition from biologically plausible artificial neuron network models of learning to a machine learning application. I develop a methodology for real-time automated reconstruction of neurons from 3D stacks of optical microscopy images. The pipeline is based on deep convolutional neural networks and includes image compression, image enhancement, segmentation of neuron cell bodies, and neurite tracing. I show that artificial neural networks can be trained to effectively compress 3D stacks of optical microscopy images and significantly enhance the intensity of neurites, making the results amenable for fast and accurate reconstruction of neurons"--Author's abstract.
Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Download or read book Neural Networks for Knowledge Representation and Inference written by Daniel S. Levine and published by Psychology Press. This book was released on 2013-04-15 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against the abilities of renewed neural network developments. More broadly than other attempts, the diverse contributions presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the history of assumed epistemologies -- both neural networks and AI -- and include several neurobiological studies of human cognition as a real system to guide the further development of artificial ones. Organized into four major sections, this volume: * outlines the history of the AI/neural network controversy, the strengths and weaknesses of both approaches, and shows the various capabilities such as generalization and discreetness as being along a broad but common continuum; * introduces several explicit, theoretical structures demonstrating the functional equivalences of neurocomputing with the staple objects of computer science and AI, such as sets and graphs; * shows variants on these types of networks that are applied in a variety of spheres, including reasoning from a geographic database, legal decision making, story comprehension, and performing arithmetic operations; * discusses knowledge representation process in living organisms, including evidence from experimental psychology, behavioral neurobiology, and electroencephalographic responses to sensory stimuli.
Download or read book Fundamentals of Neural Network Modeling written by Randolph W. Parks and published by MIT Press. This book was released on 1998 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics. The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies. Part II discusses neural network models of behavioral states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part III presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, part IV describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, and Alzheimer's disease. Contributors J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P. Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-Pierre Changeux, Kerry L. Coburn, Jonathan D. Cohen, Laurent Cohen, Jose L. Contreras-Vidal, Antonio R. Damasio, Hanna Damasio, Stanislas Dehaene, Martha J. Farah, Joaquin M. Fuster, Philippe Gaussier, Angelika Gissler, Dylan G. Harwood, Michael E. Hasselmo, J, Allan Hobson, Sam Leven, Daniel S. Levine, Debra L. Long, Roderick K. Mahurin, Raymond L. Ownby, Randolph W. Parks, Michael I. Posner, David P. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J. Tippett, Daniel Tranel, Bradley Wyble
Download or read book Associative Memory Cells Basic Units of Memory Trace written by Jin-Hui Wang and published by Springer Nature. This book was released on 2019-09-10 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on associative memory cells and their working principles, which can be applied to associative memories and memory-relevant cognitions. Providing comprehensive diagrams, it presents the author's personal perspectives on pathology and therapeutic strategies for memory deficits in patients suffering from neurological diseases and psychiatric disorders. Associative learning is a common approach to acquire multiple associated signals, including knowledge, experiences and skills from natural environments or social interaction. The identification of the cellular and molecular mechanisms underlying associative memory is important in furthering our understanding of the principles of memory formation and memory-relevant behaviors as well as in developing therapeutic strategies that enhance memory capacity in healthy individuals and improve memory deficit in patients suffering from neurological disease and psychiatric disorders. Although a series of hypotheses about neural substrates for associative memory has been proposed, numerous questions still need to be addressed, especially the basic units and their working principle in engrams and circuits specific for various memory patterns. This book summarizes the developments concerning associative memory cells reported in current and past literature, providing a valuable overview of the field for neuroscientists, psychologists and students.
Download or read book Neuronal Networks in Brain Function CNS Disorders and Therapeutics written by Carl Faingold and published by Academic Press. This book was released on 2013-12-26 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuronal Networks in Brain Function, CNS Disorders, and Therapeutics, edited by two leaders in the field, offers a current and complete review of what we know about neural networks. How the brain accomplishes many of its more complex tasks can only be understood via study of neuronal network control and network interactions. Large networks can undergo major functional changes, resulting in substantially different brain function and affecting everything from learning to the potential for epilepsy. With chapters authored by experts in each topic, this book advances the understanding of: - How the brain carries out important tasks via networks - How these networks interact in normal brain function - Major mechanisms that control network function - The interaction of the normal networks to produce more complex behaviors - How brain disorders can result from abnormal interactions - How therapy of disorders can be advanced through this network approach This book will benefit neuroscience researchers and graduate students with an interest in networks, as well as clinicians in neuroscience, pharmacology, and psychiatry dealing with neurobiological disorders. - Utilizes perspectives and tools from various neuroscience subdisciplines (cellular, systems, physiologic), making the volume broadly relevant - Chapters explore normal network function and control mechanisms, with an eye to improving therapies for brain disorders - Reflects predominant disciplinary shift from an anatomical to a functional perspective of the brain - Edited work with chapters authored by leaders in the field around the globe – the broadest, most expert coverage available
Download or read book Neural Networks written by Eric Davalo and published by Palgrave. This book was released on 1991 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are a number of different models intended to imitate some of the functions of the human brain. The authors aim to convey an intuitive and practical understanding of the topic and to provide the foundations necessary before undertaking further study.
Download or read book Discovering the Brain written by National Academy of Sciences and published by National Academies Press. This book was released on 1992-01-01 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: The brain ... There is no other part of the human anatomy that is so intriguing. How does it develop and function and why does it sometimes, tragically, degenerate? The answers are complex. In Discovering the Brain, science writer Sandra Ackerman cuts through the complexity to bring this vital topic to the public. The 1990s were declared the "Decade of the Brain" by former President Bush, and the neuroscience community responded with a host of new investigations and conferences. Discovering the Brain is based on the Institute of Medicine conference, Decade of the Brain: Frontiers in Neuroscience and Brain Research. Discovering the Brain is a "field guide" to the brainâ€"an easy-to-read discussion of the brain's physical structure and where functions such as language and music appreciation lie. Ackerman examines: How electrical and chemical signals are conveyed in the brain. The mechanisms by which we see, hear, think, and pay attentionâ€"and how a "gut feeling" actually originates in the brain. Learning and memory retention, including parallels to computer memory and what they might tell us about our own mental capacity. Development of the brain throughout the life span, with a look at the aging brain. Ackerman provides an enlightening chapter on the connection between the brain's physical condition and various mental disorders and notes what progress can realistically be made toward the prevention and treatment of stroke and other ailments. Finally, she explores the potential for major advances during the "Decade of the Brain," with a look at medical imaging techniquesâ€"what various technologies can and cannot tell usâ€"and how the public and private sectors can contribute to continued advances in neuroscience. This highly readable volume will provide the public and policymakersâ€"and many scientists as wellâ€"with a helpful guide to understanding the many discoveries that are sure to be announced throughout the "Decade of the Brain."
Download or read book Neural Plasticity and Memory written by Federico Bermudez-Rattoni and published by CRC Press. This book was released on 2007-04-17 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive, multidisciplinary review, Neural Plasticity and Memory: From Genes to Brain Imaging provides an in-depth, up-to-date analysis of the study of the neurobiology of memory. Leading specialists share their scientific experience in the field, covering a wide range of topics where molecular, genetic, behavioral, and brain imaging techniq
Download or read book A Spiking Bidirectional Associative Memory Neural Network written by Melissa Johnson and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Spiking neural networks (SNNs) are a more biologically realistic model of the brain than traditional analog neural networks and therefore should be better for modelling certain functions of the human brain. This thesis uses the concept of deriving an SNN from an accepted non-spiking neural network via analysis and modifications of the transmission function. We investigate this process to determine if and how the modifications can be made to minimize loss of information during the transition from non-spiking to spiking while retaining positive features and functionality of the non-spiking network. By comparing combinations of spiking neuron models and networks against each other, we determined that replacing the transmission function with a neural model that is similar to it allows for the easiest method to create a spiking neural network that works comparatively well. This similarity between transmission function and neuron model allows for easier parameter selection which is a key component in getting a functioning SNN. The parameters all play different roles, but for the most part, parameters that speed up spiking, such as large resistance values or small rheobases generally help the accuracy of the network. But the network is still incomplete for a spiking neural network since this conversion is often only performed after learning has been completed in analog form. The neuron model and subsequent network developed here are the initial steps in creating a bidirectional SNN that handles hetero-associative and auto-associative recall and can be switched easily between spiking and non-spiking with minimal to no loss of data. By tying everything to the transmission function, the non-spiking learning rule, which in our case uses the transmission function, and the neural model of the SNN, we are able to create a functioning SNN. Without this similarity, we find that creating SNN are much more complicated and require much more work in parameter optimization to achieve a functioning SNN.
Download or read book Associative Learning and Conditioning Theory written by Todd R Schachtman PhD and published by Oxford University Press. This book was released on 2011-06-03 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although many professionals in psychology (including the sub-disciplines of human learning and memory, clinical practice related to psychopathology, neuroscience, educational psychology and many other areas) no longer receive training in learning and conditioning, the influence of this field remains strong. Therefore, many researchers and clinicians have little knowledge about basic learning theory and its current applications beyond their own specific research topic. The primary purpose of the present volume is to highlight ways in which basic learning principles, methodology, and phenomena underpin, and indeed guide, contemporary translational research. With contributions from a distinguished collection of internationally renowned scholars, this 23-chapter volume contains specific research issues but is also broad in scope, covering a variety of topics in which associative learning and conditioning theory apply, such as drug abuse and addiction, anxiety, fear and pain research, advertising, attribution processes, acquisition of likes and dislikes, social learning, psychoneuroimmunology, and psychopathology (e.g., autism, depression, helplessness and schizophrenia). This breadth is captured in the titles of the three major sections of the book: Applications to Clinical Pathology; Applications to Health and Addiction; Applications to Cognition, Social Interaction and Motivation. The critically important phenomena and methodology of learning and conditioning continue to have a profound influence on theory and clinical concerns related to the mechanisms of memory, cognition, education, and pathology of emotional and consummatory disorders. This volume is expected to have the unique quality of serving the interests of many researchers, educators and clinicians including, for example, neuroscientists, learning and conditioning researchers, psychopharmacologists, clinical psychopathologists, and practitioners in the medical field.
Download or read book Computation Learning and Architectures written by and published by . This book was released on 1992 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book MACHINE LEARNING written by Chandra S.S., Vinod and published by PHI Learning Pvt. Ltd.. This book was released on 2021-01-01 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present book is primarily intended for undergraduate and postgraduate students of computer science and engineering, information technology, and electrical and electronics engineering. It bridges the gaps in knowledge of the seemingly difficult areas of machine learning and nature inspired computing. The text is written in a highly interactive manner, which satisfies the learning curiosity of any reader. Content of the text has been diligently organized to offer seamless learning experience. The text begins with introduction to machine learning, which is followed by explanation of different aspects of machine learning. Various supervised, unsupervised, reinforced and nature inspired learning techniques are included in the text book with numerous examples and case studies. Different aspects of new machine learning and nature inspired learning algorithms are explained in-depth. The well-explained algorithms and pseudo codes for each topic make this book useful for students. The book also throws light on areas like prediction and classification systems. Key Features • Day to day examples and pictorial representations for deeper understanding of the subject • Helps readers easily create programs/applications • Research oriented approach • More case studies and worked-out examples for each machine learning algorithm than any other book
Download or read book Opportunities in Biology written by National Research Council and published by National Academies. This book was released on 1989-01-01 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biology has entered an era in which interdisciplinary cooperation is at an all-time high, practical applications follow basic discoveries more quickly than ever before, and new technologiesâ€"recombinant DNA, scanning tunneling microscopes, and moreâ€"are revolutionizing the way science is conducted. The potential for scientific breakthroughs with significant implications for society has never been greater. Opportunities in Biology reports on the state of the new biology, taking a detailed look at the disciplines of biology; examining the advances made in medicine, agriculture, and other fields; and pointing out promising research opportunities. Authored by an expert panel representing a variety of viewpoints, this volume also offers recommendations on how to meet the infrastructure needsâ€"for funding, effective information systems, and other supportâ€"of future biology research. Exploring what has been accomplished and what is on the horizon, Opportunities in Biology is an indispensable resource for students, teachers, and researchers in all subdisciplines of biology as well as for research administrators and those in funding agencies.
Download or read book Exploratory Analysis and Data Modeling in Functional Neuroimaging written by Friedrich T. Sommer and published by MIT Press. This book was released on 2003 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of theoretical and computational approaches to neuroimaging.
Download or read book Foundations of Neural Networks Fuzzy Systems and Knowledge Engineering written by Nikola K. Kasabov and published by Marcel Alencar. This book was released on 1996 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI problems (pattern recognition, speech recognition, prediction, decision-making, game-playing) and provides illustrative examples. Includes applications in engineering, business and finance.
