During sleep, the mammalian brain generates an orderly progression of low frequency oscillations. The nature of these oscillations changes as the brain moves from sleep onset into deep sleep. Although readily measured and recorded, the underlying neural mechanisms involved and the purpose of these oscillations have remained unclear. However, as we learn more about the properties of neurons in the thalamus and cerebral cortex and their interactions, it has become possible to suggest a role for these occurrences. This book reviews the molecular components and ionic mechanisms underlying sleep oscillations, including their distorsion into epileptic seizures. It reviews the properties of ion channels, synaptic interactions, intrinsic cellular behaviour, and how these elements assemble into oscillating circuits and networks. The precision experimental data collected has provided a foundation for the study of dynamic activity in the central nervous systems and it is now possible to suggest a role for thalamocortical oscillations in memory consolidation. Thalamocortical Assemblies is for neuroscientists, neurobiologists, physiologists and other researchers interested in sleep and memory processes.

Thalamocortical Assemblies: How Ion Channels, Single Neurons and Large-Scale Networks Organize Sleep Oscillations

Health, Fitness & Dieting

Sejnowski, Terrence J.

Destexhe, Alain

Mental Health

Oxford University Press

How do groups of neurons interact to enable the organism to see, decide, and move appropriately? What are the principles whereby networks of neurons represent and compute? These are the central questions probed by The Computational Brain. Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field. The Computational Brain is the first unified and broadly accessible book to bring together computational concepts and behavioral data within a neurobiological framework.
Computer models constrained by neurobiological data can help reveal how -- networks of neurons subserve perception and behavior -- bow their physical interactions can yield global results in perception and behavior, and how their physical properties are used to code information and compute solutions. The Computational Brain focuses mainly on three domains: visual perception, learning and memory, and sensorimotor integration. Examples of recent computer models in these domains are discussed in detail, highlighting strengths and weaknesses, and extracting principles applicable to other domains. Churchland and Sejnowski show how both abstract models and neurobiologically realistic models can have useful roles in computational neuroscience, and they predict the coevolution of models and experiments at many levels of organization, from the neuron to the system.
 The Computational Brain addresses a broad audience: neuroscientists, computer scientists, cognitive scientists, and philosophers. It is written for both the expert and novice. A basic overview of neuroscience and computational theory is provided, followed by a study of some of the most recent and sophisticated modeling work in the context of relevant neurobiological research. Technical terms are clearly explained in the text, and definitions are provided in an extensive glossary. The appendix contains a précis of neurobiological techniques.
 The Computational Brain is the first unified and broadly accessible book to bring together computational concepts and behavioral data within a neurobiological framework. Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field.
 A Bradford Book
Computational Neuroscience series

The Computational Brain (Computational Neuroscience)

Computers & Technology

Churchland, Patricia Smith

Computer Science

Bradford Books

How deep learning―from Google Translate to driverless cars to personal cognitive assistants―is changing our lives and transforming every sector of the economy.
The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.
Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

The Deep Learning Revolution (Mit Press)

The MIT Press

An insightful exploration of Chat GPT and other advanced AI systems-how we got here, where we're headed, and what it all means for how we interact with the world. In Everything You Always Wanted to Know about ChatGPT, the sequel to The Deep Learning Revolution, Terrence Sejnowski offers a nuanced exploration of large language models (LLMs) like ChatGPT and what their future holds. How should we go about understanding LLMs? Do these language models truly understand what they are saying? Or is it possible that what appears to be intelligence in LLMs may be a mirror that merely reflects the intelligence of the interviewer? In this book, Sejnowski, a pioneer in computational approaches to understanding brain function, answers all our urgent questions about this astonishing new technology. Sejnowski begins by describing the debates surrounding LLMs' comprehension of language and exploring the notions of "thinking" and "intelligence." He then takes a deep dive into the historical evolution of language models, focusing on the role of transformers, the correlation between computing power and model size, and the intricate mathematics shaping LLMs. Sejnowski also provides insight into the historical roots of LLMs and discusses the potential future of AI, focusing on next-generation LLMs inspired by nature and the importance of developing energy-efficient technologies. Grounded in Sejnowski's dual expertise in AI and neuroscience, Everything You Always Wanted to Know about ChatGPT is the definitive guide to understanding the intersection of AI and human intelligence.

ChatGPT and the Future of AI: The Deep Language Revolution

Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field.
How do groups of neurons interact to enable the organism to see, decide, and move appropriately? What are the principles whereby networks of neurons represent and compute? These are the central questions probed by The Computational Brain. Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field. The Computational Brain is the first unified and broadly accessible book to bring together computational concepts and behavioral data within a neurobiological framework.
Computer models constrained by neurobiological data can help reveal how―networks of neurons subserve perception and behavior―bow their physical interactions can yield global results in perception and behavior, and how their physical properties are used to code information and compute solutions. The Computational Brain focuses mainly on three domains: visual perception, learning and memory, and sensorimotor integration. Examples of recent computer models in these domains are discussed in detail, highlighting strengths and weaknesses, and extracting principles applicable to other domains. Churchland and Sejnowski show how both abstract models and neurobiologically realistic models can have useful roles in computational neuroscience, and they predict the coevolution of models and experiments at many levels of organization, from the neuron to the system.
The Computational Brain addresses a broad audience: neuroscientists, computer scientists, cognitive scientists, and philosophers. It is written for both the expert and novice. A basic overview of neuroscience and computational theory is provided, followed by a study of some of the most recent and sophisticated modeling work in the context of relevant neurobiological research. Technical terms are clearly explained in the text, and definitions are provided in an extensive glossary. The appendix contains a précis of neurobiological techniques.
The Computational Brain is the first unified and broadly accessible book to bring together computational concepts and behavioral data within a neurobiological framework. Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field.
A Bradford Book
Computational Neuroscience series

An anniversary edition of the classic work that influenced a generation of neuroscientists and cognitive neuroscientists.
Before The Computational Brain was published in 1992, conceptual frameworks for brain function were based on the behavior of single neurons, applied globally. In The Computational Brain, Patricia Churchland and Terrence Sejnowski developed a different conceptual framework, based on large populations of neurons. They did this by showing that patterns of activities among the units in trained artificial neural network models had properties that resembled those recorded from populations of neurons recorded one at a time. It is one of the first books to bring together computational concepts and behavioral data within a neurobiological framework. Aimed at a broad audience of neuroscientists, computer scientists, cognitive scientists, and philosophers, The Computational Brain is written for both expert and novice. This anniversary edition offers a new preface by the authors that puts the book in the context of current research.
This approach influenced a generation of researchers. Even today, when neuroscientists can routinely record from hundreds of neurons using optics rather than electricity, and the 2013 White House BRAIN initiative heralded a new era in innovative neurotechnologies, the main message of The Computational Brain is still relevant.

The Computational Brain, 25th Anniversary Edition (Computational Neuroscience Series)

Science & Math

Churchland, Patricia S.

Biological Sciences

Learning How to Learn: How to Succeed in School Without Spending All Your Time Studying; A Guide for Kids and Teens

Children's Books

Oakley PhD, Barbara

Education & Reference

TarcherPerigee

Top 10 Pick for Learning Ladders' Best Books for Educators Summer 2021 A groundbreaking guide to improve teaching based on the latest research in neuroscience, from the bestselling author of A Mind for Numbers. Neuroscientists and cognitive scientists have made enormous strides in understanding the brain and how we learn, but little of that insight has filtered down to the way teachers teach. Uncommon Sense Teaching applies this research to the classroom for teachers, parents, and anyone interested in improving education. Topics include- . keeping students motivated and engaged, especially with online learning . helping students remember information long-term, so it isn't immediately forgotten after a test . how to teach inclusively in a diverse classroom where students have a wide range of abilities Drawing on research findings as well as the authors' combined decades of experience in the classroom, Uncommon Sense Teaching equips readers with the tools to enhance their teaching, whether they're seasoned professionals or parents trying to offer extra support for their children's education.

Uncommon Sense Teaching: Practical Insights in Brain Science to Help Students Learn

Rogowsky EdD, Beth

The Deep Learning Revolution

Tantor Media, Inc.

The Computational Brain, das außergewöhnliche Buch über vergleichende Forschung in den Bereichen von menschlichem Gehirn und neuesten Möglichkeiten der Computertechnologie, liegt hiermit erstmals in deutscher Sprache vor. Geschrieben von einem führenden Forscherteam in den USA, ist es eine Fundgrube für alle, die wissen wollen, was der Stand der Wissenschaft auf diesem Gebiet ist. Die Autoren führen die Bereiche der Neuroinformatik und Neurobiologie mit gut ausgesuchten Beispielen und der gebotenen Hintergrundinformation gekonnt zusammen. Das Buch wird somit nicht nur dem Fachwissenschaftler sondern auch dem interdisziplinären Interesse des Informatikers und des Biologen auf eine hervorragende Weise gerecht. Übersetzt wurde das Buch von Prof. Dr. Steffen Hölldobler und Dipl.-Biol. Claudia Hölldobler, einem Informatiker und einer Biologin. Rezension in Spektrum der Wissenschaft nr. 10, S. 122 f. im Oktober 1997 (...) Die 1992 erschienene amerikanische Originalausgabe des vorliegenden Werkes ist so erfolgreich, daß man bereits von einem Klassiker reden kann. (...) (...) ....ist das Buch sehr zu empfehlen. In Verbindung von Neurobiologie und Neuroinformatik konkurrenzlos, vermittelt es einiges von der Faszination theoretischer Hirnforschung, die auch in Deutschland zunehmend mehr Wissenschaftler in ihren Bann schlägt. Rezension erschienen in: Computer Spektrum 3/1997, S. 2 (...)Das Buch wird somit nicht nur dem Fachwissenschaftler, sondern auch den interdisziplinären Interesse des Informatikers und des Biologen auf eine hervorragende Weise gerecht(...)

Grundlagen zur Neuroinformatik und Neurobiologie: The Computational Brain in deutscher Sprache (Computational Intelligence) (German Edition)

Technology

Vieweg+Teubner Verlag

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