The Computational Brain, 25th Anniversary Edition PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download The Computational Brain, 25th Anniversary Edition PDF full book. Access full book title The Computational Brain, 25th Anniversary Edition by Patricia S. Churchland. Download full books in PDF and EPUB format.
Author: Patricia S. Churchland Publisher: MIT Press ISBN: 0262533391 Category : Science Languages : en Pages : 569
Book Description
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.
Author: Patricia S. Churchland Publisher: MIT Press ISBN: 0262533391 Category : Science Languages : en Pages : 569
Book Description
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.
Author: Patricia S. Churchland Publisher: MIT Press ISBN: 0262339668 Category : Science Languages : en Pages : 569
Book Description
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.
Author: Geoffrey Hinton Publisher: MIT Press ISBN: 9780262581684 Category : Medical Languages : en Pages : 420
Book Description
Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.
Author: C. R. Gallistel Publisher: John Wiley & Sons ISBN: 1444359762 Category : Language Arts & Disciplines Languages : en Pages : 300
Book Description
Memory and the Computational Brain offers a provocative argument that goes to the heart of neuroscience, proposing that the field can and should benefit from the recent advances of cognitive science and the development of information theory over the course of the last several decades. A provocative argument that impacts across the fields of linguistics, cognitive science, and neuroscience, suggesting new perspectives on learning mechanisms in the brain Proposes that the field of neuroscience can and should benefit from the recent advances of cognitive science and the development of information theory Suggests that the architecture of the brain is structured precisely for learning and for memory, and integrates the concept of an addressable read/write memory mechanism into the foundations of neuroscience Based on lectures in the prestigious Blackwell-Maryland Lectures in Language and Cognition, and now significantly reworked and expanded to make it ideal for students and faculty
Author: Robert Rosenbaum Publisher: MIT Press ISBN: 0262548089 Category : Science Languages : en Pages : 169
Book Description
An accessible undergraduate textbook in computational neuroscience that provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Understanding the brain is a major frontier of modern science. Given the complexity of neural circuits, advancing that understanding requires mathematical and computational approaches. This accessible undergraduate textbook in computational neuroscience provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Starting with the biophysics of single neurons, Robert Rosenbaum incrementally builds to explanations of neural coding, learning, and the relationship between biological and artificial neural networks. Examples with real neural data demonstrate how computational models can be used to understand phenomena observed in neural recordings. Based on years of classroom experience, the material has been carefully streamlined to provide all the content needed to build a foundation for modeling neural circuits in a one-semester course. Proven in the classroom Example-rich, student-friendly approach Includes Python code and a mathematical appendix reviewing the requisite background in calculus, linear algebra, and probability Ideal for engineering, science, and mathematics majors and for self-study
Author: Paul Miller Publisher: MIT Press ISBN: 0262038250 Category : Science Languages : en Pages : 405
Book Description
A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits.
Author: Michael A. Arbib Publisher: MIT Press ISBN: 0262335271 Category : Science Languages : en Pages : 810
Book Description
A comprehensive, integrated, and accessible textbook presenting core neuroscientific topics from a computational perspective, tracing a path from cells and circuits to behavior and cognition. This textbook presents a wide range of subjects in neuroscience from a computational perspective. It offers a comprehensive, integrated introduction to core topics, using computational tools to trace a path from neurons and circuits to behavior and cognition. Moreover, the chapters show how computational neuroscience—methods for modeling the causal interactions underlying neural systems—complements empirical research in advancing the understanding of brain and behavior. The chapters—all by leaders in the field, and carefully integrated by the editors—cover such subjects as action and motor control; neuroplasticity, neuromodulation, and reinforcement learning; vision; and language—the core of human cognition. The book can be used for advanced undergraduate or graduate level courses. It presents all necessary background in neuroscience beyond basic facts about neurons and synapses and general ideas about the structure and function of the human brain. Students should be familiar with differential equations and probability theory, and be able to pick up the basics of programming in MATLAB and/or Python. Slides, exercises, and other ancillary materials are freely available online, and many of the models described in the chapters are documented in the brain operation database, BODB (which is also described in a book chapter). Contributors Michael A. Arbib, Joseph Ayers, James Bednar, Andrej Bicanski, James J. Bonaiuto, Nicolas Brunel, Jean-Marie Cabelguen, Carmen Canavier, Angelo Cangelosi, Richard P. Cooper, Carlos R. Cortes, Nathaniel Daw, Paul Dean, Peter Ford Dominey, Pierre Enel, Jean-Marc Fellous, Stefano Fusi, Wulfram Gerstner, Frank Grasso, Jacqueline A. Griego, Ziad M. Hafed, Michael E. Hasselmo, Auke Ijspeert, Stephanie Jones, Daniel Kersten, Jeremie Knuesel, Owen Lewis, William W. Lytton, Tomaso Poggio, John Porrill, Tony J. Prescott, John Rinzel, Edmund Rolls, Jonathan Rubin, Nicolas Schweighofer, Mohamed A. Sherif, Malle A. Tagamets, Paul F. M. J. Verschure, Nathan Vierling-Claasen, Xiao-Jing Wang, Christopher Williams, Ransom Winder, Alan L. Yuille
Author: Terrence J. Sejnowski Publisher: MIT Press ISBN: 026203803X Category : Computers Languages : en Pages : 354
Book Description
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.
Author: Matthew Tieu Publisher: Taylor & Francis ISBN: 1000773051 Category : Psychology Languages : en Pages : 188
Book Description
What is a self? What does it mean to have selfhood? What is the relationship between selfhood and identity? These are puzzling questions that philosophers, psychologists, social scientists, and many other researchers often grapple with. Self and Identity is a book that explores and brings together relevant ideas on selfhood and identity, while also helping to clarify some important and long standing scientific and philosophical debates. It will enable readers to understand the difference between selves in humans and other animals, and the different selves that we come to possess from when we are born to when we become old. It also explains how and why the self might break down due to mental illness, thereby providing insight into how we might treat illnesses such as dementia and depression, both of which are conditions that fundamentally affect our selfhood. Taking an important step towards clarifying our understanding of human selfhood and applying it to mental illness, this book will be of great interest to researchers and postgraduate students exploring philosophical questions of selfhood, as well as those examining the connection to clinical disorders.
Author: Frances Gray Publisher: Taylor & Francis ISBN: 0429663137 Category : Psychology Languages : en Pages : 174
Book Description
Within this book, Gray argues that moments of profound existential importance are given to us in the presence of Art, and that such moments are important motivators in our personal, civic, and moral lives. Using the work of Jung, Freud, Berger, and Nussbaum, this book looks to Art in its theory and practice as a driver of psychic epiphany. Examining music, environment, architecture, poetry, and painting, it traces the relationship between the ordinary and the extraordinary, showing how we can and do locate ourselves beyond our own psyches in a world of artistic endeavour. Gray concludes that Art plays a critical role in psychological practice and human flourishing on an individual and collective level. Epiphanies, Individuation, and Human Flourishing will appeal to artists, art theorists, therapists, and analysts as a teaching tool that demonstrates the possible connections that can be made among the arts, sciences and psycho-therapeutic communities, and Nature.
Author: Patricia S. Churchland
Author: Patricia S. Churchland
Author: Patricia S. Churchland
Author: Geoffrey Hinton
Author: C. R. Gallistel
Author: Robert Rosenbaum
Author: Paul Miller
Author: Michael A. Arbib
Author: Terrence J. Sejnowski
Author: Matthew Tieu
Author: Frances Gray