Edmund T. Rolls
- Published in print:
- 2007
- Published Online:
- September 2009
- ISBN:
- 9780199232703
- eISBN:
- 9780191724046
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199232703.001.0001
- Subject:
- Neuroscience, Behavioral Neuroscience
This book presents a unified approach to understanding memory, attention, and decision-making. It shows how these fundamental functions for cognitive neuroscience can be understood in a common and ...
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This book presents a unified approach to understanding memory, attention, and decision-making. It shows how these fundamental functions for cognitive neuroscience can be understood in a common and unifying computational neuroscience framework. This framework links empirical research on brain function from neurophysiology, functional neuroimaging, and the effects of brain damage, to a description of how neural networks in the brain implement these functions using a set of common principles. The book describes the principles of operation of these networks, and how they could implement such important functions as memory, attention, and decision-making. The book discusses the hippocampus and memory, reward- and punishment-related learning, emotion and motivation, invariant visual object recognition learning, short-term memory, attention, biased competition, probabilistic decision-making, action selection, and decision-making.Less
This book presents a unified approach to understanding memory, attention, and decision-making. It shows how these fundamental functions for cognitive neuroscience can be understood in a common and unifying computational neuroscience framework. This framework links empirical research on brain function from neurophysiology, functional neuroimaging, and the effects of brain damage, to a description of how neural networks in the brain implement these functions using a set of common principles. The book describes the principles of operation of these networks, and how they could implement such important functions as memory, attention, and decision-making. The book discusses the hippocampus and memory, reward- and punishment-related learning, emotion and motivation, invariant visual object recognition learning, short-term memory, attention, biased competition, probabilistic decision-making, action selection, and decision-making.
Tomaso Poggio
- Published in print:
- 2010
- Published Online:
- August 2013
- ISBN:
- 9780262514620
- eISBN:
- 9780262289610
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262514620.003.0009
- Subject:
- Psychology, Vision
This chapter discusses a vision of computational neuroscience, and also addresses what has happened since the publication of David Marr’s Vision. It argues that it is now time to reemphasize the ...
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This chapter discusses a vision of computational neuroscience, and also addresses what has happened since the publication of David Marr’s Vision. It argues that it is now time to reemphasize the links between levels of understanding, if progress in computational neuroscience is wanted. It is believed that computational neuroscience over the past thirty years can be described as mostly exploring each level of understanding independent of the others. Some of the past research trends in computational neuroscience are explained. This brief look at computational neuroscience in the last decades indicates that significant progress has been made at each of the levels of understanding—in a sense following Marr’s prescription—though the problem is far from being solved. It argues that neuroscience can help computational theory and even computer science.Less
This chapter discusses a vision of computational neuroscience, and also addresses what has happened since the publication of David Marr’s Vision. It argues that it is now time to reemphasize the links between levels of understanding, if progress in computational neuroscience is wanted. It is believed that computational neuroscience over the past thirty years can be described as mostly exploring each level of understanding independent of the others. Some of the past research trends in computational neuroscience are explained. This brief look at computational neuroscience in the last decades indicates that significant progress has been made at each of the levels of understanding—in a sense following Marr’s prescription—though the problem is far from being solved. It argues that neuroscience can help computational theory and even computer science.
Patricia S. Churchland and Terrence J. Sejnowski
- Published in print:
- 2016
- Published Online:
- January 2018
- ISBN:
- 9780262533393
- eISBN:
- 9780262339650
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262533393.003.0001
- Subject:
- Psychology, Cognitive Neuroscience
This book introduces a conceptual framework for brain function based on large populations of neurons. It advances the hypothesis that emergent properties are high-level effects that depend on ...
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This book introduces a conceptual framework for brain function based on large populations of neurons. It advances the hypothesis that emergent properties are high-level effects that depend on lower-level phenomena in some systematic way, drawing on the idea that brains are computational in nature. Areas and topics related to computational neuroscience covered in this book include computational mechanisms in neurons, analysis of signal processing in neural circuits, representation of sensory information, systems models of sensorimotor integration, and computational approaches to plasticity. The book emphasizes the importance of single neuron models as the foundation into which network models must eventually fit. It also provides a background discussion on neuroscience and the science of computation.Less
This book introduces a conceptual framework for brain function based on large populations of neurons. It advances the hypothesis that emergent properties are high-level effects that depend on lower-level phenomena in some systematic way, drawing on the idea that brains are computational in nature. Areas and topics related to computational neuroscience covered in this book include computational mechanisms in neurons, analysis of signal processing in neural circuits, representation of sensory information, systems models of sensorimotor integration, and computational approaches to plasticity. The book emphasizes the importance of single neuron models as the foundation into which network models must eventually fit. It also provides a background discussion on neuroscience and the science of computation.
Edmund T. Rolls
- Published in print:
- 2013
- Published Online:
- January 2014
- ISBN:
- 9780199659890
- eISBN:
- 9780191772078
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199659890.001.0001
- Subject:
- Neuroscience, Behavioral Neuroscience, Development
What produces emotions? Why do we have emotions? How do we have emotions? Why do emotional states feel like something? What is the relation between emotion, and reward value, and subjective feelings ...
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What produces emotions? Why do we have emotions? How do we have emotions? Why do emotional states feel like something? What is the relation between emotion, and reward value, and subjective feelings of pleasure? How is the value of a good represented in the brain? Will neuroeconomics replace classical microeconomics? How does the brain implement decision-making? Are gene-defined rewards and emotions in the interests of the genes, and does rational multistep planning enable us to go beyond selfish genes to long-term plans and social contracts in the interests of the individual? This book seeks explanations of emotion and decision-making by considering these questions.Less
What produces emotions? Why do we have emotions? How do we have emotions? Why do emotional states feel like something? What is the relation between emotion, and reward value, and subjective feelings of pleasure? How is the value of a good represented in the brain? Will neuroeconomics replace classical microeconomics? How does the brain implement decision-making? Are gene-defined rewards and emotions in the interests of the genes, and does rational multistep planning enable us to go beyond selfish genes to long-term plans and social contracts in the interests of the individual? This book seeks explanations of emotion and decision-making by considering these questions.
Andreas Heinz
- Published in print:
- 2017
- Published Online:
- May 2018
- ISBN:
- 9780262036894
- eISBN:
- 9780262342841
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262036894.001.0001
- Subject:
- Neuroscience, Behavioral Neuroscience
For many psychiatric disorders, neurobiological findings do not help to diagnose a specific disease or to predict its outcome. This book suggests to take a new look at mental disorders by using ...
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For many psychiatric disorders, neurobiological findings do not help to diagnose a specific disease or to predict its outcome. This book suggests to take a new look at mental disorders by using computational models to better understand human decision making. It shows how such models can be applied to basic learning mechanisms that cut across established nosological boundaries of mental disorders. Such a computational and dimensional approach focuses on the malleability of human behavior and its biological underpinnings. The book argues that this computational and dimensional approach can help to promote and focus neurobiological research, however, it does not replace an anthropological understanding of clinical questions including the definition of mental disorders and ethical considerations. This is illustrated by describing the new understanding of mental disorders with respect to clinical and neuro-computational aspects of psychosis, affective and addictive disorders.Less
For many psychiatric disorders, neurobiological findings do not help to diagnose a specific disease or to predict its outcome. This book suggests to take a new look at mental disorders by using computational models to better understand human decision making. It shows how such models can be applied to basic learning mechanisms that cut across established nosological boundaries of mental disorders. Such a computational and dimensional approach focuses on the malleability of human behavior and its biological underpinnings. The book argues that this computational and dimensional approach can help to promote and focus neurobiological research, however, it does not replace an anthropological understanding of clinical questions including the definition of mental disorders and ethical considerations. This is illustrated by describing the new understanding of mental disorders with respect to clinical and neuro-computational aspects of psychosis, affective and addictive disorders.
Patricia S. Churchland and Terrence J. Sejnowski
- Published in print:
- 2016
- Published Online:
- January 2018
- ISBN:
- 9780262533393
- eISBN:
- 9780262339650
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262533393.003.0002
- Subject:
- Psychology, Cognitive Neuroscience
This chapter provides an overview of the “neuroscience” component of the “computational neuroscience” synergy. It begins with a discussion of three ideas about levels in nervous systems: levels of ...
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This chapter provides an overview of the “neuroscience” component of the “computational neuroscience” synergy. It begins with a discussion of three ideas about levels in nervous systems: levels of analysis, levels of organization, and levels of processing. Levels of organization are essentially anatomical, and refer to a hierarchy of components and to structures that comprise these components. Levels of processing are physiological, and refer to the location of a process relative to the transducers and muscles. Levels of analysis are conceptual, and refer to different kinds of questions asked about how the brain performs a task. The chapter proceeds by considering seven categories of structural organization in nervous systems: systems, topographic maps, layers and columns, local networks, neurons, synapses, and molecules. It concludes by presenting a short list of brain facts.Less
This chapter provides an overview of the “neuroscience” component of the “computational neuroscience” synergy. It begins with a discussion of three ideas about levels in nervous systems: levels of analysis, levels of organization, and levels of processing. Levels of organization are essentially anatomical, and refer to a hierarchy of components and to structures that comprise these components. Levels of processing are physiological, and refer to the location of a process relative to the transducers and muscles. Levels of analysis are conceptual, and refer to different kinds of questions asked about how the brain performs a task. The chapter proceeds by considering seven categories of structural organization in nervous systems: systems, topographic maps, layers and columns, local networks, neurons, synapses, and molecules. It concludes by presenting a short list of brain facts.
Zeb Kurth-Nelson, John P. O’Doherty, Deanna M. Barch, Sophie Denève, Daniel Durstewitz, Michael J. Frank, Joshua A. Gordon, Sanjay J. Mathew, Yael Niv, Kerry Ressler, and Heike Tost
- Published in print:
- 2016
- Published Online:
- May 2017
- ISBN:
- 9780262035422
- eISBN:
- 9780262337854
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262035422.003.0005
- Subject:
- Psychology, Cognitive Neuroscience
Vast spectra of biological and psychological processes are potentially involved in the mechanisms of psychiatric illness. Computational neuroscience brings a diverse toolkit to bear on understanding ...
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Vast spectra of biological and psychological processes are potentially involved in the mechanisms of psychiatric illness. Computational neuroscience brings a diverse toolkit to bear on understanding these processes. This chapter begins by organizing the many ways in which computational neuroscience may provide insight to the mechanisms of psychiatric illness. It then contextualizes the quest for deep mechanistic understanding through the perspective that even partial or nonmechanistic understanding can be applied productively. Finally, it questions the standards by which these approaches should be evaluated. If computational psychiatry hopes to go beyond traditional psychiatry, it cannot be judged solely on the basis of how closely it reproduces the diagnoses and prognoses of traditional psychiatry, but must also be judged against more fundamental measures such as patient outcomes.Less
Vast spectra of biological and psychological processes are potentially involved in the mechanisms of psychiatric illness. Computational neuroscience brings a diverse toolkit to bear on understanding these processes. This chapter begins by organizing the many ways in which computational neuroscience may provide insight to the mechanisms of psychiatric illness. It then contextualizes the quest for deep mechanistic understanding through the perspective that even partial or nonmechanistic understanding can be applied productively. Finally, it questions the standards by which these approaches should be evaluated. If computational psychiatry hopes to go beyond traditional psychiatry, it cannot be judged solely on the basis of how closely it reproduces the diagnoses and prognoses of traditional psychiatry, but must also be judged against more fundamental measures such as patient outcomes.
Michael J. Frank
- Published in print:
- 2016
- Published Online:
- May 2017
- ISBN:
- 9780262035422
- eISBN:
- 9780262337854
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262035422.003.0006
- Subject:
- Psychology, Cognitive Neuroscience
Advances in our understanding of brain function and dysfunction require the integration of heterogeneous sources of data across multiple levels of analysis, from biophysics to cognition and back. ...
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Advances in our understanding of brain function and dysfunction require the integration of heterogeneous sources of data across multiple levels of analysis, from biophysics to cognition and back. This chapter reviews the utility of computational neuroscience approaches across these levels and how they have advanced our understanding of multiple constructs relevant for mental illness, including working memory, reward-based decision making, model-free and model-based reinforcement learning, exploration versus exploitation, Pavlovian contributions to motivated behavior, inhibitory control, and social interactions. The computational framework formalizes these processes, providing quantitative and falsifiable predictions. It also affords a characterization of mental illnesses not in terms of overall deficit but rather in terms of aberrations in managing fundamental trade-offs inherent within healthy cognitive processing.Less
Advances in our understanding of brain function and dysfunction require the integration of heterogeneous sources of data across multiple levels of analysis, from biophysics to cognition and back. This chapter reviews the utility of computational neuroscience approaches across these levels and how they have advanced our understanding of multiple constructs relevant for mental illness, including working memory, reward-based decision making, model-free and model-based reinforcement learning, exploration versus exploitation, Pavlovian contributions to motivated behavior, inhibitory control, and social interactions. The computational framework formalizes these processes, providing quantitative and falsifiable predictions. It also affords a characterization of mental illnesses not in terms of overall deficit but rather in terms of aberrations in managing fundamental trade-offs inherent within healthy cognitive processing.
David M. Kaplan
- Published in print:
- 2017
- Published Online:
- January 2018
- ISBN:
- 9780199685509
- eISBN:
- 9780191765667
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780199685509.003.0008
- Subject:
- Philosophy, Philosophy of Mind, Philosophy of Science
There is an ongoing philosophical and scientific debate concerning the nature of computational explanation in the neurosciences. Recently, some have cited modeling work involving so-called canonical ...
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There is an ongoing philosophical and scientific debate concerning the nature of computational explanation in the neurosciences. Recently, some have cited modeling work involving so-called canonical neural computations—standard computational modules that apply the same fundamental operations across multiple brain areas—as evidence that computational neuroscientists sometimes employ a distinctive explanatory scheme from that of mechanistic explanation. Because these neural computations can rely on diverse circuits and mechanisms, modeling the underlying mechanisms is supposed to be of limited explanatory value. I argue that these conclusions about computational explanations in neuroscience are mistaken, and rest upon a number of confusions about the proper scope of mechanistic explanation and the relevance of multiple realizability considerations. Once these confusions are resolved, the mechanistic character of computational explanations can once again be appreciated.Less
There is an ongoing philosophical and scientific debate concerning the nature of computational explanation in the neurosciences. Recently, some have cited modeling work involving so-called canonical neural computations—standard computational modules that apply the same fundamental operations across multiple brain areas—as evidence that computational neuroscientists sometimes employ a distinctive explanatory scheme from that of mechanistic explanation. Because these neural computations can rely on diverse circuits and mechanisms, modeling the underlying mechanisms is supposed to be of limited explanatory value. I argue that these conclusions about computational explanations in neuroscience are mistaken, and rest upon a number of confusions about the proper scope of mechanistic explanation and the relevance of multiple realizability considerations. Once these confusions are resolved, the mechanistic character of computational explanations can once again be appreciated.
Larry R. Squire
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780195396133
- eISBN:
- 9780199918409
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195396133.003.0011
- Subject:
- Neuroscience, History of Neuroscience
John Moore initially became known for elucidating the action of tetrodotoxin and other neurotoxins using his innovative sucrose gap method for voltage clamping squid axon. He also was a pioneer in ...
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John Moore initially became known for elucidating the action of tetrodotoxin and other neurotoxins using his innovative sucrose gap method for voltage clamping squid axon. He also was a pioneer in the nascent area of computational neuroscience, using computer simulations in parallel with experiments to predict experimental results and thus validate the concepts used in modeling. Intrigued by the possibility of applying his knowledge of physics to learn how neurons employ electricity to generate and transmit signals, he led the field in exploring how ion channels and neuronal morphology affect excitation and signal propagation. He developed electronic instrumentation of high precision for electrophysiology, the result of experience gained through an unconventional career path: early training in physics, assignments involving feedback in the Manhattan Project, and learning principles of operational amplifiers at the RCA Laboratories. His summers at the Marine Biological Laboratory in Woods Hole, MA, now exceeding 50, made much of his work possible and established the MBL as his intellectual home. In retirement, he developed the educational software Neurons In Action, coauthored with his wife Ann Stuart, that is now widely used as a learning tool in neurophysiology.Less
John Moore initially became known for elucidating the action of tetrodotoxin and other neurotoxins using his innovative sucrose gap method for voltage clamping squid axon. He also was a pioneer in the nascent area of computational neuroscience, using computer simulations in parallel with experiments to predict experimental results and thus validate the concepts used in modeling. Intrigued by the possibility of applying his knowledge of physics to learn how neurons employ electricity to generate and transmit signals, he led the field in exploring how ion channels and neuronal morphology affect excitation and signal propagation. He developed electronic instrumentation of high precision for electrophysiology, the result of experience gained through an unconventional career path: early training in physics, assignments involving feedback in the Manhattan Project, and learning principles of operational amplifiers at the RCA Laboratories. His summers at the Marine Biological Laboratory in Woods Hole, MA, now exceeding 50, made much of his work possible and established the MBL as his intellectual home. In retirement, he developed the educational software Neurons In Action, coauthored with his wife Ann Stuart, that is now widely used as a learning tool in neurophysiology.
Edmund T. Rolls
- Published in print:
- 2001
- Published Online:
- March 2012
- ISBN:
- 9780198524885
- eISBN:
- 9780191689277
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198524885.003.0013
- Subject:
- Psychology, Vision
This concluding chapter sums up the key findings of this study on the computational neuroscience of vision. The results show that the responses of many inferior temporal visual cortex neurons have ...
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This concluding chapter sums up the key findings of this study on the computational neuroscience of vision. The results show that the responses of many inferior temporal visual cortex neurons have transform invariant responses to objects and faces, but not all neurons have view invariance. The findings also indicate that much of the information available from the responses of the neurons about shapes and objects is available in short time periods and that invariant representations can be self-organized using a trace learning rule incorporated in a feature hierarchy network such as VisNet.Less
This concluding chapter sums up the key findings of this study on the computational neuroscience of vision. The results show that the responses of many inferior temporal visual cortex neurons have transform invariant responses to objects and faces, but not all neurons have view invariance. The findings also indicate that much of the information available from the responses of the neurons about shapes and objects is available in short time periods and that invariant representations can be self-organized using a trace learning rule incorporated in a feature hierarchy network such as VisNet.
John H. Krystal, Alan Anticevic, John D. Murray, David Glahn, Naomi Driesen, Genevieve Yang, and Xiao-Jing Wang
- Published in print:
- 2016
- Published Online:
- May 2017
- ISBN:
- 9780262035422
- eISBN:
- 9780262337854
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262035422.003.0016
- Subject:
- Psychology, Cognitive Neuroscience
Clinical heterogeneity presents important challenges to optimizing psychiatric diagnoses and treatments. Patients clustered within current diagnostic schema vary widely on many features of their ...
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Clinical heterogeneity presents important challenges to optimizing psychiatric diagnoses and treatments. Patients clustered within current diagnostic schema vary widely on many features of their illness, including their responses to treatments. As outlined by the American Psychiatric Association Diagnostic and Statistical Manual (DSM), psychiatric diagnoses have been refined since DSM was introduced in 1952. These diagnoses serve as the targets for current treatments and supported the emergence of psychiatric genomics. However, the Research Domain Criteria highlight DSM’s shortcomings, including its limited ability to encompass dimensional features linking patients across diagnoses. This chapter considers elements of the dimensional and categorical features of psychiatric diagnoses, with a particular focus on schizophrenia. It highlights ways that computational neuroscience approaches have shed light on both dimensional and categorical features of the biology of schizophrenia. It also considers opportunities and challenges associated with attempts to reduce clinical heterogeneity through categorical and dimensional approaches to clustering patients. Finally, discussion will consider ways that one might work with both approaches in parallel or sequentially, as well as diagnostic schema that might integrate both perspectives.Less
Clinical heterogeneity presents important challenges to optimizing psychiatric diagnoses and treatments. Patients clustered within current diagnostic schema vary widely on many features of their illness, including their responses to treatments. As outlined by the American Psychiatric Association Diagnostic and Statistical Manual (DSM), psychiatric diagnoses have been refined since DSM was introduced in 1952. These diagnoses serve as the targets for current treatments and supported the emergence of psychiatric genomics. However, the Research Domain Criteria highlight DSM’s shortcomings, including its limited ability to encompass dimensional features linking patients across diagnoses. This chapter considers elements of the dimensional and categorical features of psychiatric diagnoses, with a particular focus on schizophrenia. It highlights ways that computational neuroscience approaches have shed light on both dimensional and categorical features of the biology of schizophrenia. It also considers opportunities and challenges associated with attempts to reduce clinical heterogeneity through categorical and dimensional approaches to clustering patients. Finally, discussion will consider ways that one might work with both approaches in parallel or sequentially, as well as diagnostic schema that might integrate both perspectives.
Edmund T. Rolls
- Published in print:
- 2001
- Published Online:
- March 2012
- ISBN:
- 9780198524885
- eISBN:
- 9780191689277
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198524885.003.0001
- Subject:
- Psychology, Vision
The introduction discusses the coverage of this book, which is about the computational neuroscience of vision. It introduces some of the background for understanding brain computation and discusses ...
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The introduction discusses the coverage of this book, which is about the computational neuroscience of vision. It introduces some of the background for understanding brain computation and discusses how some of the essential features of this can be captured by simple formalisms. The introduction also explores three neuronal network architectures, long-term potentiation and long-term depression, and the fine structure of the cerebral neocortex.Less
The introduction discusses the coverage of this book, which is about the computational neuroscience of vision. It introduces some of the background for understanding brain computation and discusses how some of the essential features of this can be captured by simple formalisms. The introduction also explores three neuronal network architectures, long-term potentiation and long-term depression, and the fine structure of the cerebral neocortex.
Argye E. Hillis
- Published in print:
- 2005
- Published Online:
- March 2012
- ISBN:
- 9780198526544
- eISBN:
- 9780191689420
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198526544.003.0022
- Subject:
- Psychology, Neuropsychology
A theory of cognitive rehabilitation should specify how change from a damaged state of cognitive processing can be modified into a normal, or ...
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A theory of cognitive rehabilitation should specify how change from a damaged state of cognitive processing can be modified into a normal, or more functional, state of cognitive processing. Such a theory should incorporate what is known about the cognitive representations and processes underlying normal cognition, how these are affected by brain damage, and how learning or modification of cognitive processing occurs. This chapter argues that development of a useful theory of cognitive rehabilitation requires integrating advances from cognitive neuropsychology, experimental psychology, computational neuroscience, and molecular biology of the brain, as well as empirical evidence from various branches of rehabilitation. It is likely that such a theory will specify how behavioral rehabilitation strategies can be augmented by pharmacological agents.Less
A theory of cognitive rehabilitation should specify how change from a damaged state of cognitive processing can be modified into a normal, or more functional, state of cognitive processing. Such a theory should incorporate what is known about the cognitive representations and processes underlying normal cognition, how these are affected by brain damage, and how learning or modification of cognitive processing occurs. This chapter argues that development of a useful theory of cognitive rehabilitation requires integrating advances from cognitive neuropsychology, experimental psychology, computational neuroscience, and molecular biology of the brain, as well as empirical evidence from various branches of rehabilitation. It is likely that such a theory will specify how behavioral rehabilitation strategies can be augmented by pharmacological agents.
Edmund T. Rolls
- Published in print:
- 2016
- Published Online:
- November 2016
- ISBN:
- 9780198784852
- eISBN:
- 9780191836299
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198784852.001.0001
- Subject:
- Neuroscience, Molecular and Cellular Systems, Behavioral Neuroscience
The aim of this book is to provide insight into the principles of operation of the cerebral cortex. These principles are key to understanding how we, as humans, function. There have been few previous ...
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The aim of this book is to provide insight into the principles of operation of the cerebral cortex. These principles are key to understanding how we, as humans, function. There have been few previous attempts to set out some of the important principles of operation of the cortex, and this book is pioneering. The book goes beyond separate connectional neuroanatomical, neurophysiological, neuroimaging, neuropsychiatric, and computational neuroscience approaches, by combining evidence from all these areas to formulate hypotheses about how and what the cerebral cortex computes. As clear hypotheses are needed in this most important area of 21st century science, how our brains work, I have formulated a set of hypotheses about the principles of cortical operation to guide thinking and future research. The book focusses on the principles of operation of the cerebral cortex, because at this time it is possible to propose and describe many principles, and many are likely to stand the test of time, and provide a foundation for further developments, even if some need to be changed. In this context, I have not attempted to produce an overall theory of operation of the cerebral cortex, because at this stage of our understanding, such a theory would be incorrect or incomplete. However, many of the principles described will provide the foundations for more complete theories of the operation of the cerebral cortex. This book is intended to provide a foundation for future understanding, and it is hoped that future work will develop and add to these principles of operation of the cerebral cortex. The book includes Appendices on the operation of many of the neuronal networks described in the book, together with simulation software written in Matlab.Less
The aim of this book is to provide insight into the principles of operation of the cerebral cortex. These principles are key to understanding how we, as humans, function. There have been few previous attempts to set out some of the important principles of operation of the cortex, and this book is pioneering. The book goes beyond separate connectional neuroanatomical, neurophysiological, neuroimaging, neuropsychiatric, and computational neuroscience approaches, by combining evidence from all these areas to formulate hypotheses about how and what the cerebral cortex computes. As clear hypotheses are needed in this most important area of 21st century science, how our brains work, I have formulated a set of hypotheses about the principles of cortical operation to guide thinking and future research. The book focusses on the principles of operation of the cerebral cortex, because at this time it is possible to propose and describe many principles, and many are likely to stand the test of time, and provide a foundation for further developments, even if some need to be changed. In this context, I have not attempted to produce an overall theory of operation of the cerebral cortex, because at this stage of our understanding, such a theory would be incorrect or incomplete. However, many of the principles described will provide the foundations for more complete theories of the operation of the cerebral cortex. This book is intended to provide a foundation for future understanding, and it is hoped that future work will develop and add to these principles of operation of the cerebral cortex. The book includes Appendices on the operation of many of the neuronal networks described in the book, together with simulation software written in Matlab.
Mark Crooks
- Published in print:
- 2008
- Published Online:
- August 2013
- ISBN:
- 9780262232661
- eISBN:
- 9780262286497
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262232661.003.0012
- Subject:
- Philosophy, General
This chapter discusses psychoneural identity theory and how it aims to discredit sensory phenomenology—or qualia—for philosophical realist and reductionist programs. Paul and Patricia Churchland’s ...
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This chapter discusses psychoneural identity theory and how it aims to discredit sensory phenomenology—or qualia—for philosophical realist and reductionist programs. Paul and Patricia Churchland’s works are exemplary of such motivation. Paul Churchland’s philosophizing of computational neuroscience attempts to resolve mental contents into vector coding and its transformations, yet what he describes is not phenomenology but a sensory schema of psychology. Patricia Churchland, on the other hand, admits there are few or no intertheoretic identities, and therefore no proper analogies from them to her projected psychology-to-neuroscience reduction. Their misrepresentations of the nature of perception are documented here, with a conclusion that the dogmatic denial of phenomenology by reductionist philosophy heretofore involves invalid and unsound arguments.Less
This chapter discusses psychoneural identity theory and how it aims to discredit sensory phenomenology—or qualia—for philosophical realist and reductionist programs. Paul and Patricia Churchland’s works are exemplary of such motivation. Paul Churchland’s philosophizing of computational neuroscience attempts to resolve mental contents into vector coding and its transformations, yet what he describes is not phenomenology but a sensory schema of psychology. Patricia Churchland, on the other hand, admits there are few or no intertheoretic identities, and therefore no proper analogies from them to her projected psychology-to-neuroscience reduction. Their misrepresentations of the nature of perception are documented here, with a conclusion that the dogmatic denial of phenomenology by reductionist philosophy heretofore involves invalid and unsound arguments.
Paul Fletcher and Aikaterini Fotopoulou
- Published in print:
- 2015
- Published Online:
- September 2015
- ISBN:
- 9780190267278
- eISBN:
- 9780190267308
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780190267278.003.0016
- Subject:
- Psychology, Cognitive Psychology, Social Psychology
Sense of agency—the feeling of being the author of one’s actions—may be a critical component of one’s sense of self and of one’s interaction with the world. Insights from clinical and experimental ...
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Sense of agency—the feeling of being the author of one’s actions—may be a critical component of one’s sense of self and of one’s interaction with the world. Insights from clinical and experimental neuropsychology, as well as cognitive and computational neuroscience, have provided complementary evidence that the sense of agency arises from the integration of an array of internal and external cues. These frameworks can help to explain how disruptions in one or more of these cues may result in altered experiences of agency. This chapter reviews these explanatory frameworks and shows how important and useful they have become in making sense of an array of clinical observations, from the disorders of control and agency that result from circumscribed brain damage to the widespread attenuation of agency that may characterize psychosis in which no clear brain lesion has been identified.Less
Sense of agency—the feeling of being the author of one’s actions—may be a critical component of one’s sense of self and of one’s interaction with the world. Insights from clinical and experimental neuropsychology, as well as cognitive and computational neuroscience, have provided complementary evidence that the sense of agency arises from the integration of an array of internal and external cues. These frameworks can help to explain how disruptions in one or more of these cues may result in altered experiences of agency. This chapter reviews these explanatory frameworks and shows how important and useful they have become in making sense of an array of clinical observations, from the disorders of control and agency that result from circumscribed brain damage to the widespread attenuation of agency that may characterize psychosis in which no clear brain lesion has been identified.
Joel Z. Leibo and Tomaso Poggio
- Published in print:
- 2018
- Published Online:
- June 2018
- ISBN:
- 9780199674923
- eISBN:
- 9780191842702
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780199674923.003.0025
- Subject:
- Neuroscience, Sensory and Motor Systems, Development
This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex ...
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This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now accurate enough to be installed as safety devices in (for now) human-driven vehicles and the speech recognition systems embedded in smartphones have become increasingly impressive. While not being entirely biologically based, we note that computational neuroscience, as described in this chapter, makes up a considerable portion of such systems’ intellectual pedigree.Less
This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now accurate enough to be installed as safety devices in (for now) human-driven vehicles and the speech recognition systems embedded in smartphones have become increasingly impressive. While not being entirely biologically based, we note that computational neuroscience, as described in this chapter, makes up a considerable portion of such systems’ intellectual pedigree.
Patricia S. Churchland and Terrence J. Sejnowski
- Published in print:
- 2016
- Published Online:
- January 2018
- ISBN:
- 9780262533393
- eISBN:
- 9780262339650
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262533393.003.0007
- Subject:
- Psychology, Cognitive Neuroscience
This concluding chapter explores future avenues for research and what remains to be done if the computer-modeling projects aimed at understanding the mysteries of the brain are to progress. In ...
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This concluding chapter explores future avenues for research and what remains to be done if the computer-modeling projects aimed at understanding the mysteries of the brain are to progress. In particular, it considers the problem of constructing synthetic brains and the reasons why the long-range project of understanding how the brain works should engender such constructive ambitions. It also discusses three ways of addressing the constructive problem: Carver Mead's strategy of building artificial neural structures, such as retinas and cochleas, using silicon-based CMOS VLSI technology; Dana Ballard's method of integrating perception with motor control; and Rodney Brook's method which involves making mobots capable of getting around in the world using limited reflex repertoires. The chapter concludes with an assessment of theoretical and ethical questions about what to do with the knowledge gained from computational neuroscience.Less
This concluding chapter explores future avenues for research and what remains to be done if the computer-modeling projects aimed at understanding the mysteries of the brain are to progress. In particular, it considers the problem of constructing synthetic brains and the reasons why the long-range project of understanding how the brain works should engender such constructive ambitions. It also discusses three ways of addressing the constructive problem: Carver Mead's strategy of building artificial neural structures, such as retinas and cochleas, using silicon-based CMOS VLSI technology; Dana Ballard's method of integrating perception with motor control; and Rodney Brook's method which involves making mobots capable of getting around in the world using limited reflex repertoires. The chapter concludes with an assessment of theoretical and ethical questions about what to do with the knowledge gained from computational neuroscience.
Philip Husbands, Owen Holland, and Michael Wheeler
- Published in print:
- 2008
- Published Online:
- August 2013
- ISBN:
- 9780262083775
- eISBN:
- 9780262256384
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262083775.003.0019
- Subject:
- Computer Science, Artificial Intelligence
Jack Cowan, educated at Edinburgh University, Imperial College, and MIT, is one of the pioneers of continuous approaches to neural networks and brain modeling. He has made many important ...
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Jack Cowan, educated at Edinburgh University, Imperial College, and MIT, is one of the pioneers of continuous approaches to neural networks and brain modeling. He has made many important contributions to machine learning, neural networks, and computational neuroscience. This chapter presents an edited transcript of an interview conducted on the 6 November 2006. Cowan offers a unique perspective on activity in machine intelligence in the UK and the US in the late 1950s and early 1960s. He recounts how his ideas developed under the influence of some of the great pioneers of cybernetics, and how those ideas flourished throughout his subsequent career.Less
Jack Cowan, educated at Edinburgh University, Imperial College, and MIT, is one of the pioneers of continuous approaches to neural networks and brain modeling. He has made many important contributions to machine learning, neural networks, and computational neuroscience. This chapter presents an edited transcript of an interview conducted on the 6 November 2006. Cowan offers a unique perspective on activity in machine intelligence in the UK and the US in the late 1950s and early 1960s. He recounts how his ideas developed under the influence of some of the great pioneers of cybernetics, and how those ideas flourished throughout his subsequent career.
