The Computational Brain 25th Anniversary Edition
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The Computational Brain, 25th Anniversary Edition
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
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.
Modeling Neural Circuits Made Simple with Python
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