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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.
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
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.
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.
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.
The Ecological Brain is the first book of its kind, using complexity science to integrate the seemingly disparate fields of ecological psychology and neuroscience. The book develops a unique framework for unifying investigations and explanations of mind that span brain, body, and environment: the NeuroEcological Nexus Theory (NExT). Beginning with an introduction to the history of the fields, the author provides an assessment of why ecological psychology and neuroscience are commonly viewed as irreconcilable methods for investigating and explaining cognition, intelligent behavior, and the systems that realize them. The book then progresses to its central aim: presenting a unified investigative and explanatory framework offering concepts, methods, and theories applicable across neural and ecological scales of investigation. By combining the core principles of ecological psychology, neural population dynamics, and synergetics under a unified complexity science approach, NExT offers a compressive investigative framework to explain and understand neural, bodily, and environmental contributions to perception-action and other forms of intelligent behavior and thought. The book progresses the conversation around the role of brains in ecological psychology, as well as bodies and environments in neuroscience. It is essential reading for all students of ecological psychology, perception, cognitive sciences, and neuroscience, as well as anyone interested in the history and philosophy of the brain/mind sciences and their state-of-the-art methods and theories.
This volume establishes the conceptual foundation for sustained investigation into tool development in neuroscience. Neuroscience relies on diverse and sophisticated experimental tools, and its ultimate explanatory target—our brains and hence the organ driving our behaviors—catapults the investigation of these research tools into a philosophical spotlight. The chapters in this volume integrate the currently scattered work on tool development in neuroscience into the broader philosophy of science community. They also present an accessible compendium for neuroscientists interested in the broader theoretical dimensions of their experimental practices. The chapters are divided into five thematic sections. Section 1 discusses the development of revolutionary research tools across neuroscience’s history and argues to various conclusions concerning the relationship between new research tools and theory progress in neuroscience. Section 2 shows how a focus on research tools and their development in neuroscience transforms some traditional epistemological issues and questions about knowledge production in philosophy of science. Section 3 speaks to the most general questions about the way we characterize the nature of the portion of the world that this science addresses. Section 4 discusses hybrid research tools that integrate laboratory and computational methods in exciting new ways. Finally, Section 5 extends research on tool development to the related science of genetics. The Tools of Neuroscience Experiment will be of interest to philosophers and philosophically minded scientists working at the intersection of philosophy and neuroscience.
The neuron doctrine, first formulated in 1891, states that the brain is constructed of individual neurons, organized into functioning circuits that mediate behavior. It is the fundamental principal that underlies all of neuroscience and clinical neurology. Foundations of the Neuron Doctrine gives an authoritative account of how this theory was the product of an explosion of histological studies and vigorous debates near the end of the nineteenth century by an extraordinary group of scientists, led by Santiago Ramon y Cajal of Spain, using a selective stain discovered by Camillo Golgi of Italy. They were the first to describe the distinctive branching patterns of nerve cells, providing evidence that the cells interact as individual units to form circuits, opposed however by Golgi, who held out for a view that the nerve cells form syncytial networks. Studies in the 1950s appeared to confirm the nerve cell as an individual unit, as embodied in the neuron doctrine, which became the basis for the rise of concepts of normal and disordered neural function since then. This 25th Anniversary Edition is timely. Recent studies are showing a much greater degree of complexity in neuronal organization, so that the debate of neuron versus network is again coming to the fore in neuroscience research. Unique to this Anniversary Edition is the inclusion of commentaries by distinguished international leaders - Marina Bentivoglio, Xavier De Felipe, Sten Grillner, Paolo Mazzarello, Larry Swanson, and Rafael Yuste - on the continuing relevance of the neuron doctrine for modern studies of the brain at all levels, from genes and molecules to microcircuits, neural networks, and behavior. As this new wave of modern studies expands our concepts of nervous function as the basis of behavior, Foundations of the Neuron Doctrine will be a unique source providing conceptual continuity from classical times to the present and into the future. With commentaries from Marina Bentivoglio Paolo Mazzarello Javier DeFelipe Larry Swanson Sten Grillner Rafael Yuste
There is at present no publication specifically dedicated to analyzing the philosophical implications of augmented reality, especially regarding knowledge formation, which constitutes a fundamental trait of knowledge society. That is why this volume includes an analysis of the applications and implications of augmented reality. While applications cover diverse fields like psychopathology and education, implications concern issues as diverse as negative knowledge, group cognition, the internet of things, and ontological issues, among others. In this way, it is intended not only to generate answers, but also, to draw attention to new problems that arise with the diffusion of augmented reality. In order to contemplate these problems from diverse perspectives, the auhors are from a variety of fields - philosophy, computer sciencess, education, psychology, and many more. Accordingly, the volume offers varied and interesting contributions which are of interest to professionals from multiple disciplines.
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.