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Circuits of the Mind
In this groundbreaking work, computer scientist Leslie G. Valiant details a promising new computational approach to studying the intricate workings of the human brain. Focusing on the brain's enigmatic ability to access a massive store of accumulated information very quickly during reasoning processes, the author asks how such feats are possible given the extreme constraints imposed by the brain's finite number of neurons, their limited speed of communication, and their restricted interconnectivity. Valiant proposes a "neuroidal model" that serves as a vehicle to explore these fascinating questions.
While embracing the now classic theories of McCulloch and Pitts, the neuroidal model also accommodates state information in the neurons, more flexible timing mechanisms, a variety of assumptions about interconnectivity, and the possibility that different areas perform different functions. Programmable so that a wide range of algorithmic theories can be described and evaluated, the model provides a concrete computational language and a unified framework in which diverse cognitive phenomena--such as memory, learning, and reasoning--can be systematically and concurrently analyzed.
Included in this volume is a new preface that highlights some remarkable points of agreement between the neuroidal model and findings in neurobiology made since that model's original publication. Experiments have produced strong evidence for the theory's predictions about the existence of strong synapses in cortex and about the use of precise timing mechanisms within and between neurons. The theory also provides a quantitative explanation of how randomly placed neurons can be harnessed as resources for general purpose learning and memory--and is therefore synergistic with the striking recent discovery of neurogenesis in cortex.
Requiring no specialized knowledge, Circuits of the Mind , masterfully offers an exciting new approach to brain science for students and researchers in computer science, neurobiology, neuroscience, artificial intelligence, and cognitive science.
While embracing the now classic theories of McCulloch and Pitts, the neuroidal model also accommodates state information in the neurons, more flexible timing mechanisms, a variety of assumptions about interconnectivity, and the possibility that different areas perform different functions. Programmable so that a wide range of algorithmic theories can be described and evaluated, the model provides a concrete computational language and a unified framework in which diverse cognitive phenomena--such as memory, learning, and reasoning--can be systematically and concurrently analyzed.
Included in this volume is a new preface that highlights some remarkable points of agreement between the neuroidal model and findings in neurobiology made since that model's original publication. Experiments have produced strong evidence for the theory's predictions about the existence of strong synapses in cortex and about the use of precise timing mechanisms within and between neurons. The theory also provides a quantitative explanation of how randomly placed neurons can be harnessed as resources for general purpose learning and memory--and is therefore synergistic with the striking recent discovery of neurogenesis in cortex.
Requiring no specialized knowledge, Circuits of the Mind , masterfully offers an exciting new approach to brain science for students and researchers in computer science, neurobiology, neuroscience, artificial intelligence, and cognitive science.
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First published November 24, 1994
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About the author
Leslie Valiant
9 books15 followersLeslie Valiant FRS is a British computer scientist and computational theorist. He is currently the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University, and was educated at King's College, Cambridge, Imperial College London, and University of Warwick where he received a PhD in computer science in 1974.
Valiant is world-renowned for his work in theoretical computer science. Among his many contributions to complexity theory, he introduced the notion of #P-completeness to explain why enumeration and reliability problems are intractable. He also introduced the "probably approximately correct" (PAC) model of machine learning that has helped the field of computational learning theory grow, and the concept of holographic algorithms. His earlier work in automata theory includes an algorithm for context-free parsing, which is (as of 2010) still the asymptotically fastest known. He also works in computational neuroscience focusing on understanding memory and learning.
Valiant's 2013 book is Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World (Basic Books, ISBN 9780465032716). In it he argues, among other things, that evolutionary biology does not explain the rate at which evolution occurs, writing, for example, "The evidence for Darwin's general schema for evolution being essentially correct is convincing to the great majority of biologists. This author has been to enough natural history museums to be convinced himself. All this, however, does not mean the current theory of evolution is adequately explanatory. At present the theory of evolution can offer no account of the rate at which evolution progresses to develop complex mechanisms or to maintain them in changing environments."
Valiant is world-renowned for his work in theoretical computer science. Among his many contributions to complexity theory, he introduced the notion of #P-completeness to explain why enumeration and reliability problems are intractable. He also introduced the "probably approximately correct" (PAC) model of machine learning that has helped the field of computational learning theory grow, and the concept of holographic algorithms. His earlier work in automata theory includes an algorithm for context-free parsing, which is (as of 2010) still the asymptotically fastest known. He also works in computational neuroscience focusing on understanding memory and learning.
Valiant's 2013 book is Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World (Basic Books, ISBN 9780465032716). In it he argues, among other things, that evolutionary biology does not explain the rate at which evolution occurs, writing, for example, "The evidence for Darwin's general schema for evolution being essentially correct is convincing to the great majority of biologists. This author has been to enough natural history museums to be convinced himself. All this, however, does not mean the current theory of evolution is adequately explanatory. At present the theory of evolution can offer no account of the rate at which evolution progresses to develop complex mechanisms or to maintain them in changing environments."
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September 14, 2024AUTHOR PROPOSES A "NEUROIDAL MODEL" TO DESCRIBE BRAIN PROCESSES
Leslie Valiant is a Professor of Computer Science and Applied Mathematics at Harvard University.
He states in the Preface to this 1994 book, "The main task for the present, therefore, may be viewed as a prescientific one. What is the most promising way to proceed in order to find the intellectual structure within which at least some central questions can be formulated and reduced to problem solving? This volume suggests one avenue. It places at the center of the investigation some simple tasks of memory and learning, and advocates that these tasks be investigated by means of detailed computational models."
He says, "The aspect of reasoning that we are particularly concerned with here is so called commonsense reasoning. This is the process that humans use to cope with the mundane but complex aspects of the world in evaluating everyday situations. It is reasoning that is generally done subconsciously. It is perhaps precisely because we have no awareness of these processes that it has proved to difficult to simulate them in machines. No one has yet made a home cleaning robot that can execute its task with reasonable flexibility and commonsense." (Pg. 159)
Later, he notes: "The major stumbling block at first seemed to be the philosophical problem raised by inductive learning, an aspect of cognition that seems impossible to evade. We believe now that computational learning theory gives an adequate view on this. It explains how it may be possible for a system to learn to cope in a world that is too complex for it to describe or understand." (Pg. 203-204)
This book will be of some interest to those looking for creative models of the brain.
Leslie Valiant is a Professor of Computer Science and Applied Mathematics at Harvard University.
He states in the Preface to this 1994 book, "The main task for the present, therefore, may be viewed as a prescientific one. What is the most promising way to proceed in order to find the intellectual structure within which at least some central questions can be formulated and reduced to problem solving? This volume suggests one avenue. It places at the center of the investigation some simple tasks of memory and learning, and advocates that these tasks be investigated by means of detailed computational models."
He says, "The aspect of reasoning that we are particularly concerned with here is so called commonsense reasoning. This is the process that humans use to cope with the mundane but complex aspects of the world in evaluating everyday situations. It is reasoning that is generally done subconsciously. It is perhaps precisely because we have no awareness of these processes that it has proved to difficult to simulate them in machines. No one has yet made a home cleaning robot that can execute its task with reasonable flexibility and commonsense." (Pg. 159)
Later, he notes: "The major stumbling block at first seemed to be the philosophical problem raised by inductive learning, an aspect of cognition that seems impossible to evade. We believe now that computational learning theory gives an adequate view on this. It explains how it may be possible for a system to learn to cope in a world that is too complex for it to describe or understand." (Pg. 203-204)
This book will be of some interest to those looking for creative models of the brain.
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June 15, 2010Ronald de Wolf recommended this book not because he's read it, but because he think Leslie Valiant is a TCS whiz.
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