Computing The Mind

In a culmination of humanity's millennia-long quest for self knowledge, the sciences of the mind are now in a position to offer concrete, empirically validated answers to the most fundamental questions about human nature. What does it mean to be a mind? How is the mind related to the brain? How are minds shaped by their embodiment and environment? What are the principles behind cognitive functions such as perception, memory, language, thought, and consciousness?By analyzing the tasks facing any sentient being that is subject to stimulation and a pressure to act, Shimon Edelman identifies computation as the common denominator in the emerging answers to all these questions. Any system composed of elements that exchange signals with each other and occasionally with the rest of the world can be said to be engaged in computation. A brain composed of neurons is one example of a system that computes, and the computations that the neurons collectively carry out constitute the brain's mind.Edelman presents a computational account of the entire spectrum of cognitive phenomena that constitutes the mind. He begins with sentience, and uses examples from visual perception to demonstrate that it must, at its very core, be a type of computation. Throughout his account, Edelman acknowledges the human mind's biological origins. Along the way, he also demystifies traits such as creativity, language, and individual and collective consciousness, and hints at how naturally evolved minds can transcend some of their limitations by moving to computational substrates other than brains. The account that Edelman gives in this book is accessible, yet unified and rigorous, and the big picture he presents is supported by evidence ranging from neurobiology to computer science. The book should be read by anyone seeking a comprehensive and current introduction to cognitive psychology.

Computational approaches dominate contemporary cognitive science, promising a unified, scientific explanation of how the mind works. However, computational approaches raise major philosophical and scientific questions. In what sense is the mind computational? How do computational approaches explain perception, learning, and decision making? What kinds of challenges should computational approaches overcome to advance our understanding of mind, brain, and behaviour? The Routledge Handbook of the Computational Mind is an outstanding overview and exploration of these issues and the first philosophical collection of its kind. Comprising thirty-five chapters by an international team of contributors from different disciplines, the Handbook is organised into four parts: History and future prospects of computational approaches Types of computational approach Foundations and challenges of computational approaches Applications to specific parts of psychology. Essential reading for students and researchers in philosophy of mind, philosophy of psychology, and philosophy of science, The Routledge Handbook of the Computational Mind will also be of interest to those studying computational models in related subjects such as psychology, neuroscience, and computer science.

This book represents the views of one of the greatest mathematicians of the twentieth century on the analogies between computing machines and the living human brain. John von Neumann concludes that the brain operates in part digitally, in part analogically, but uses a peculiar statistical language unlike that employed in the operation of man-made computers. This edition includes a new foreword by two eminent figures in the fields of philosophy, neuroscience, and consciousness.

Winner of the Wolf Prize for his contribution to our understanding of the universe, Penrose takes on the question of whether artificial intelligence will ever approach the intricacy of the human mind. 144 illustrations.

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

Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks

Presents the author's thesis that consciousness, in its manifestation in the human quality of understanding, is doing something that mere computation cannot; and attempts to understand how such non-computational action might arise within scientifically comprehensive physical laws.

Mind computation is a hot topic of intelligence science. It is explored by computing to explain the theoretical basis of human intelligence. Through long-term research, a mind model CAM (Consciousness and Memory) is proposed, which provides a general framework for brain-like intelligence and brain-like intelligent systems.This novel book centers on mind model CAM, systematically discusses the theoretical basis of mind computation in nine chapters. Because of its advanced progresses on brain-like intelligence, it is useful as a primary reference volume for professionals and graduate students in intelligence science, cognitive science and artificial intelligence.

Brain and mind continue to be a topic of enormous scientific interest. With the recent advances in measuring instruments such as two-photon laser scanning microscopy and fMRI, the neuronal connectivity and circuitry of how the brain's various regions are hierarchically interconnected and organized are better understood now than ever before. By reverse engineering the brain, computer scientists hope to build cognitively intelligent systems that will revolutionize the artificial intelligence paradigm. Brain-Mind Machinery provides a walkthrough to the world of brain-inspired computing and mind-related questions. Bringing together diverse viewpoints and expertise from multidisciplinary communities, the book explores the human quest to build a thinking machine with human-like capabilities. Readers will acquire a first-hand understanding of the brain and mind mechanisms and machineries, as well as how much we have progressed in and how far we are from building a truly general intelligent system like the human brain.

Brain and mind continue to be a topic of enormous scientific interest. With the recent advances in measuring instruments such as two-photon laser scanning microscopy and fMRI, the neuronal connectivity and circuitry of how the brain's various regions are hierarchically interconnected and organized are better understood now than ever before. By reverse engineering the brain, computer scientists hope to build cognitively intelligent systems that will revolutionize the artificial intelligence paradigm. Brain-Mind Machinery provides a walkthrough to the world of brain-inspired computing and mind-related questions. Bringing together diverse viewpoints and expertise from multidisciplinary communities, the book explores the human quest to build a thinking machine with human-like capabilities. Readers will acquire a first-hand understanding of the brain and mind mechanisms and machineries, as well as how much we have progressed in and how far we are from building a truly general intelligent system like the human brain.