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Artificial Intelligence

Artificial Intelligence
Author: F. H. George
Publisher: Routledge
Total Pages: 230
Release: 2018-05-15
Genre: Psychology
ISBN: 135110957X

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Originally published in 1986, in order to probe, dispute and analyse the role of artificial intelligence in cybernetic thought and information science, the author pursues this topic within its philosophical, behavioral and neurophysiological contexts, while drawing attention to cognitive issues. By elucidating the problems and potential associated with knowledge-based systems, the book emphasized the need to examine artificial intelligence in its own right.


The Neuron in Context

The Neuron in Context
Author: Vanessa Lux
Publisher: Springer
Total Pages: 0
Release: 2024-04-26
Genre: Psychology
ISBN: 9783031552281

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Neuroscience has largely abandoned its localizationist and mechanistic framework of the 20th century. The plastic, embodied, and network character of our nervous system is widely acknowledged and systems theory approaches to consciousness dominate the field. However, the underlying neuron theory has not changed. The neuron doctrine, conceptualizing the single neuron as atomistic, one-directional source of neural function, still provides the template for our understanding of these basic elements of our nervous system and the material foundation of consciousness. Yet, the single neuron does not exist as an isolated unit. It is embedded within multiple cellular, structural, and functional contexts, and highly depends on them for its development, neural activity, and survival. The book discusses the constraints of the neuron doctrine and its pragmatic reductionism in the light of the growing knowledge about the brain’s connectivity, plasticity, and systemic and embodied nature. To overcome these constraints, the author argues for a new neuron theory, depicting the neuron as bidirectional hub which is at the same time source and product of neural function. This bidirectionality is further characterized by spatial and time dimensions, placing the neuron within a multi-level pathway model of psychobiological development from the perspective of Developmental Embodiment Research. Furthermore, the author discusses the potential of neuroepigenetic markers to characterize the neuron and its range of plasticity within this developmental perspective.With its focus on neuroepigenetics, the book addresses a knowledge gap in the current study of the neural foundations of psychological functions. The multi-level and bidirectional perspective is already realized in approaches coming from developmental systems theory, which model neural function at the connectome level, and it also fits with approaches investigating feedback loops underlying neural activity at the single cell level. At both these levels, the spatial and the time dimensions are well characterized, either as changing connectivity patterns across different age groups, or as synaptic feedback loops underlying neural activation patterns. However, for the intermediate level of small neural populations, which is currently the main target for studies investigating the neural basis of specific psychological functions, this characterization turned out to be more challenging. Multi-cell recordings have provided a first glimpse into the complex interaction patterns of these small neural networks, but they are limited to the recording period and do not provide information about the long-term developmental and activation history. Here, neuroepigenetic markers could be of use. Due to their relative stability and, at the same time, environmental sensitivity, neuroepigenetic markers represent an additional layer of information in which, to a certain degree, the cell’s metabolic and activation history is aggregated over time. This information is available at the single neuron level but could also be modeled as aggregated information for small neural populations and the supporting cellular context. Looking through this “epigenetic lens” adds to our understanding of the neuron as bidirectional hub by emphasizing the molecular correlates of functional stabilization and their contextual prerequisites. These prerequisites reach from the immediate cellular context to the social-cultural contexts which shape the culturally specific modes of acquisition of psychological functions throughout the lifespan. Accounting for this multilayered contextuality of the neuron and its function affords to repositions the relationship between neuroscience and psychology in their joint effort to unravel the material basis of consciousness. This provides new challenges but also new perspectives for theoretical psychology. The book presents these current developments and debates to researchers, graduate students, and interested professionals and practitioners working in neuroscience, epigenetics, psychiatry, psychology and psychotherapy. It also provides a basic introduction into neuroepigenetics, its mechanisms, and first findings for graduate students as well as interested professionals and practitioners working in psychiatry, psychology, and psychotherapy.


Multivariate Statistical Machine Learning Methods for Genomic Prediction

Multivariate Statistical Machine Learning Methods for Genomic Prediction
Author: Osval Antonio Montesinos López
Publisher: Springer Nature
Total Pages: 707
Release: 2022-02-14
Genre: Technology & Engineering
ISBN: 3030890104

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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.


From Neurons to Neighborhoods

From Neurons to Neighborhoods
Author: National Research Council
Publisher: National Academies Press
Total Pages: 610
Release: 2000-11-13
Genre: Social Science
ISBN: 0309069882

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How we raise young children is one of today's most highly personalized and sharply politicized issues, in part because each of us can claim some level of "expertise." The debate has intensified as discoveries about our development-in the womb and in the first months and years-have reached the popular media. How can we use our burgeoning knowledge to assure the well-being of all young children, for their own sake as well as for the sake of our nation? Drawing from new findings, this book presents important conclusions about nature-versus-nurture, the impact of being born into a working family, the effect of politics on programs for children, the costs and benefits of intervention, and other issues. The committee issues a series of challenges to decision makers regarding the quality of child care, issues of racial and ethnic diversity, the integration of children's cognitive and emotional development, and more. Authoritative yet accessible, From Neurons to Neighborhoods presents the evidence about "brain wiring" and how kids learn to speak, think, and regulate their behavior. It examines the effect of the climate-family, child care, community-within which the child grows.


Discovering the Brain

Discovering the Brain
Author: National Academy of Sciences
Publisher: National Academies Press
Total Pages: 195
Release: 1992-01-01
Genre: Medical
ISBN: 0309045290

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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."


The Brain in Context

The Brain in Context
Author: Jonathan D. Moreno
Publisher: Columbia University Press
Total Pages: 212
Release: 2019-11-26
Genre: Medical
ISBN: 0231547102

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The human brain is the most complex object in the known universe. The field of neuroscience has made remarkable strides in recent years in understanding aspects of the brain, yet we still struggle with seemingly fundamental questions about how the brain works. What lessons can we learn from neuroscience’s successes and failures? What kinds of questions can neuroscience answer, and what will remain out of reach? In The Brain in Context, the bioethicist Jonathan D. Moreno and the neuroscientist Jay Schulkin provide an accessible and thought-provoking account of the evolution of neuroscience and the neuroscience of evolution. They emphasize that the brain is not an isolated organ—it extends into every part of the body and every aspect of human life. Understanding the brain requires studying the environmental, biological, chemical, genetic, and social factors that continue to shape it. Moreno and Schulkin describe today’s transformative devices, theories, and methods, including technologies like fMRI and optogenetics as well as massive whole-brain activity maps and the attempt to create a digital simulation of the brain. They show how theorizing about the brain and experimenting with it often go hand in hand, and they raise cautions about unintended consequences of technological interventions. The Brain in Context is a stimulating and even-handed assessment of the scope and limits of what we know about how we think.


EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection
Author: Sandeep Kumar Satapathy
Publisher: Academic Press
Total Pages: 134
Release: 2019-02-10
Genre: Medical
ISBN: 0128174277

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EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification. Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers Provides a number of experimental analyses, with their results discussed and appropriately validated


A Discovery of Neural Network Architectures for Context-dependent Computations

A Discovery of Neural Network Architectures for Context-dependent Computations
Author: Doris Voina
Publisher:
Total Pages: 0
Release: 2022
Genre:
ISBN:

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All human and animal behavior from seeing, hearing, running, and falling in love, is the result of complex dynamics in a web of intricate networks in the brain. The human brain, in particular, contains close to 100 billion brain cells (or neurons) of different types connected through more than 100 trillion connections (or synapses), often in complicated patterns (or motifs) that depend on the brain area and function of the network. How these neurons and synapses are organized into specific network architectures so that neuronal activity and dynamics can give rise to behavior is still a mystery. A similar problem exists in the case of artificial neural networks: there is no systematic approach to designing artificial network architectures that generalize well across tasks, conditions, and contexts. For artificial and biological networks alike, we are interested in understanding the building blocks that permit a broad array of neural network functionality to emerge. We approach this problem from several perspectives: 1) we show how a biologically inspired microcircuit with several specific features (multiple inhibitory cell types, a comparatively smaller neuron population recurrently connected to the network that acts in a switch-like manner, and a disinhibitory network motif) is a minimally complex architecture that can switch between visual processing of the static context and the moving context; 2) we find a fast and flexible artificial network with a biologically-inspired network motif that generalizes across context when classifying visual stimuli shown sequentially and with different background contexts; 3) we begin the process of identifying new, bio-inspired network motifs via methods that identifynetwork motifs that inform neuron type classification. Our work clarifies the set of network connection structures that are both necessary and sufficient to achieve more flexible computational capability in both biological and artificial neural networks.


Deep Learning

Deep Learning
Author: Ian Goodfellow
Publisher: MIT Press
Total Pages: 801
Release: 2016-11-10
Genre: Computers
ISBN: 0262337371

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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.


Talking Nets

Talking Nets
Author: James A. Anderson
Publisher: MIT Press
Total Pages: 452
Release: 2000-02-28
Genre: Medical
ISBN: 9780262511117

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Surprising tales from the scientists who first learned how to use computers to understand the workings of the human brain. Since World War II, a group of scientists has been attempting to understand the human nervous system and to build computer systems that emulate the brain's abilities. Many of the early workers in this field of neural networks came from cybernetics; others came from neuroscience, physics, electrical engineering, mathematics, psychology, even economics. In this collection of interviews, those who helped to shape the field share their childhood memories, their influences, how they became interested in neural networks, and what they see as its future. The subjects tell stories that have been told, referred to, whispered about, and imagined throughout the history of the field. Together, the interviews form a Rashomon-like web of reality. Some of the mythic people responsible for the foundations of modern brain theory and cybernetics, such as Norbert Wiener, Warren McCulloch, and Frank Rosenblatt, appear prominently in the recollections. The interviewees agree about some things and disagree about more. Together, they tell the story of how science is actually done, including the false starts, and the Darwinian struggle for jobs, resources, and reputation. Although some of the interviews contain technical material, there is no actual mathematics in the book. Contributors James A. Anderson, Michael Arbib, Gail Carpenter, Leon Cooper, Jack Cowan, Walter Freeman, Stephen Grossberg, Robert Hecht-Neilsen, Geoffrey Hinton, Teuvo Kohonen, Bart Kosko, Jerome Lettvin, Carver Mead, David Rumelhart, Terry Sejnowski, Paul Werbos, Bernard Widrow