Mark L. Latash
- Published in print:
- 2008
- Published Online:
- May 2009
- ISBN:
- 9780195333169
- eISBN:
- 9780199864195
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195333169.003.0008
- Subject:
- Neuroscience, Sensory and Motor Systems, Techniques
The final part of the book addresses two issues: modeling of synergies and possible synergic organization of non-motor functions such as the language and the sensory function. Within the first issue, ...
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The final part of the book addresses two issues: modeling of synergies and possible synergic organization of non-motor functions such as the language and the sensory function. Within the first issue, relations of synergies to the control theory is discussed with a brief overview of the central issues of the control theory such as open-loop and closed-loop control, and optimal control. Two models of synergies are described in more detail. One of them offers a neural network with back-coupling loops as the mechanism for multi-digit synergies. The other one suggests a scheme within which synergies emerge without any explicit feedback mechanisms. Further, the focus shifts to two aspects of synergies within the equilibrium-point hypothesis. One of them suggests that the principle of equilibrium-point control can by itself lead to synergies. The other deals with possible synergies in the hierarchy of control variables within the reference configuration hypothesis. The next two sections in this Part develop the notion of synergies for the sensory systems and for the production of human language. Multi-sensory interactions and synesthesia are described as possible reflections of sensory synergies. The book ends with an overview of its main points and a list of unsolved problems.Less
The final part of the book addresses two issues: modeling of synergies and possible synergic organization of non-motor functions such as the language and the sensory function. Within the first issue, relations of synergies to the control theory is discussed with a brief overview of the central issues of the control theory such as open-loop and closed-loop control, and optimal control. Two models of synergies are described in more detail. One of them offers a neural network with back-coupling loops as the mechanism for multi-digit synergies. The other one suggests a scheme within which synergies emerge without any explicit feedback mechanisms. Further, the focus shifts to two aspects of synergies within the equilibrium-point hypothesis. One of them suggests that the principle of equilibrium-point control can by itself lead to synergies. The other deals with possible synergies in the hierarchy of control variables within the reference configuration hypothesis. The next two sections in this Part develop the notion of synergies for the sensory systems and for the production of human language. Multi-sensory interactions and synesthesia are described as possible reflections of sensory synergies. The book ends with an overview of its main points and a list of unsolved problems.
Michael S. A. Graziano
- Published in print:
- 2009
- Published Online:
- May 2009
- ISBN:
- 9780195326703
- eISBN:
- 9780199864867
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195326703.003.0011
- Subject:
- Neuroscience, Sensory and Motor Systems, Behavioral Neuroscience
This chapter begins by describing some previously proposed models of the neuronal control of movement. These models include the λ model and the hypothesis of muscle synergies. The chapter then ...
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This chapter begins by describing some previously proposed models of the neuronal control of movement. These models include the λ model and the hypothesis of muscle synergies. The chapter then discusses the usefulness of higher-order sources of information about the periphery such as visual feedback and internally generated models of the body. The chapter ends with a schematic version of the cortical-spinal-muscle system and a discussion of how electrical stimulation in cortex is likely to affect this circuitry and result in complex movement.Less
This chapter begins by describing some previously proposed models of the neuronal control of movement. These models include the λ model and the hypothesis of muscle synergies. The chapter then discusses the usefulness of higher-order sources of information about the periphery such as visual feedback and internally generated models of the body. The chapter ends with a schematic version of the cortical-spinal-muscle system and a discussion of how electrical stimulation in cortex is likely to affect this circuitry and result in complex movement.
Domitilla Del Vecchio and Richard M. Murray
- Published in print:
- 2014
- Published Online:
- October 2017
- ISBN:
- 9780691161532
- eISBN:
- 9781400850501
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691161532.003.0003
- Subject:
- Biology, Biochemistry / Molecular Biology
This chapter turns to some of the tools from dynamical systems and feedback control theory that will be used in the rest of the text to analyze and design biological circuits. It first models the ...
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This chapter turns to some of the tools from dynamical systems and feedback control theory that will be used in the rest of the text to analyze and design biological circuits. It first models the dynamics of a system using the input/output modeling formalism described in Chapter 1 and then studies the “robustness” of the system of a given function of the circuit. The chapter then discusses some of the underlying ideas for how to model biological oscillatory behavior, focusing on those types of oscillations that are most common in biomolecular systems. Hereafter, the chapter explores how the location of equilibrium points, their stability, their regions of attraction, and other dynamic phenomena vary based on the values of the parameters in a model. Finally, methods for reducing the complexity of the models that are introduced in this chapter are reviewed.Less
This chapter turns to some of the tools from dynamical systems and feedback control theory that will be used in the rest of the text to analyze and design biological circuits. It first models the dynamics of a system using the input/output modeling formalism described in Chapter 1 and then studies the “robustness” of the system of a given function of the circuit. The chapter then discusses some of the underlying ideas for how to model biological oscillatory behavior, focusing on those types of oscillations that are most common in biomolecular systems. Hereafter, the chapter explores how the location of equilibrium points, their stability, their regions of attraction, and other dynamic phenomena vary based on the values of the parameters in a model. Finally, methods for reducing the complexity of the models that are introduced in this chapter are reviewed.
Moody T. Chu and Gene H. Golub
- Published in print:
- 2005
- Published Online:
- September 2007
- ISBN:
- 9780198566649
- eISBN:
- 9780191718021
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566649.003.0002
- Subject:
- Mathematics, Applied Mathematics
Inverse eigenvalue problems arise in a remarkable variety of applications. This chapter briefly highlights a few applications. The discussion is divided into six categories of applications: feedback ...
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Inverse eigenvalue problems arise in a remarkable variety of applications. This chapter briefly highlights a few applications. The discussion is divided into six categories of applications: feedback control, applied mechanics, inverse Sturm-Liouville problem, applied physics, numerical analysis, and signal and data processing. Each category covers some additional problems.Less
Inverse eigenvalue problems arise in a remarkable variety of applications. This chapter briefly highlights a few applications. The discussion is divided into six categories of applications: feedback control, applied mechanics, inverse Sturm-Liouville problem, applied physics, numerical analysis, and signal and data processing. Each category covers some additional problems.
Rafal Goebel, Ricardo G. Sanfelice, and Andrew R. Teel
- Published in print:
- 2012
- Published Online:
- October 2017
- ISBN:
- 9780691153896
- eISBN:
- 9781400842636
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691153896.003.0004
- Subject:
- Mathematics, Applied Mathematics
This chapter discusses the effect of state perturbations on solutions to a hybrid system. It shows that state perturbations, of arbitrarily small size, can dramatically change the behavior of ...
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This chapter discusses the effect of state perturbations on solutions to a hybrid system. It shows that state perturbations, of arbitrarily small size, can dramatically change the behavior of solutions. While such a phenomenon is also present in continuous-time and discrete-time dynamical systems, it is magnified in the hybrid setting, due to the flows and the jumps being constrained to the flow and the jump sets, respectively. Perturbations affecting the whole state of a hybrid system are usually considered. The resulting behaviors are quite representative of what may occur if perturbations come from state measurement error in a hybrid feedback control system or from errors present in numerical simulations of hybrid systems. The case of hybrid feedback control is given some attention in this chapter. Also, throughout the chapter, effects of perturbations are related to the regularity properties of the data of hybrid systems.Less
This chapter discusses the effect of state perturbations on solutions to a hybrid system. It shows that state perturbations, of arbitrarily small size, can dramatically change the behavior of solutions. While such a phenomenon is also present in continuous-time and discrete-time dynamical systems, it is magnified in the hybrid setting, due to the flows and the jumps being constrained to the flow and the jump sets, respectively. Perturbations affecting the whole state of a hybrid system are usually considered. The resulting behaviors are quite representative of what may occur if perturbations come from state measurement error in a hybrid feedback control system or from errors present in numerical simulations of hybrid systems. The case of hybrid feedback control is given some attention in this chapter. Also, throughout the chapter, effects of perturbations are related to the regularity properties of the data of hybrid systems.
Brian P. Ingalls, Tau-Mu Yi, and Pablo A. Iglesias
- Published in print:
- 2006
- Published Online:
- August 2013
- ISBN:
- 9780262195485
- eISBN:
- 9780262257060
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262195485.003.0012
- Subject:
- Mathematics, Mathematical Biology
This chapter presents a framework ideally suited to an analysis of dynamic systems. Tools from control theory can be applied to elucidate the functioning of self-regulating (homeostatic) systems and ...
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This chapter presents a framework ideally suited to an analysis of dynamic systems. Tools from control theory can be applied to elucidate the functioning of self-regulating (homeostatic) systems and to predict the effect of perturbations. It begins with an introduction to the framework of linear systems and one of the primary tools for describing their behavior: the frequency response. It then discusses how one particular type of control system, integral feedback control, plays a crucial role in both homeostasis and sensory adaptation. Next, it considers other uses of feedback mechanisms in cell signaling pathways.Less
This chapter presents a framework ideally suited to an analysis of dynamic systems. Tools from control theory can be applied to elucidate the functioning of self-regulating (homeostatic) systems and to predict the effect of perturbations. It begins with an introduction to the framework of linear systems and one of the primary tools for describing their behavior: the frequency response. It then discusses how one particular type of control system, integral feedback control, plays a crucial role in both homeostasis and sensory adaptation. Next, it considers other uses of feedback mechanisms in cell signaling pathways.
George F. R. Ellis
- Published in print:
- 2008
- Published Online:
- October 2011
- ISBN:
- 9780199544318
- eISBN:
- 9780191701351
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199544318.003.0004
- Subject:
- Religion, Theology, Philosophy of Religion
This chapter outlines a view of emergent reality in which it is clear that non-physical quantities such as information and goals can have physical effect in the world of particles and forces. It ...
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This chapter outlines a view of emergent reality in which it is clear that non-physical quantities such as information and goals can have physical effect in the world of particles and forces. It explains how complexity emerges at higher levels of the hierarchy of structure on the basis of the underlying physics, leading to emergent behaviours that cannot be reduced to a description at any lower level. The first key to handling complexity is hierarchical physical structuring and function. Such functioning involves the combination of bottom-up and top-down action in the hierarchy of structure. The second key to the emergence of truly complex properties is the role of hierarchically structured information in setting goals via feedback control systems. The development of complexity in living systems requires both evolutionary processes acting over very long time periods and developmental processes acting over much shorter times.Less
This chapter outlines a view of emergent reality in which it is clear that non-physical quantities such as information and goals can have physical effect in the world of particles and forces. It explains how complexity emerges at higher levels of the hierarchy of structure on the basis of the underlying physics, leading to emergent behaviours that cannot be reduced to a description at any lower level. The first key to handling complexity is hierarchical physical structuring and function. Such functioning involves the combination of bottom-up and top-down action in the hierarchy of structure. The second key to the emergence of truly complex properties is the role of hierarchically structured information in setting goals via feedback control systems. The development of complexity in living systems requires both evolutionary processes acting over very long time periods and developmental processes acting over much shorter times.
Wassim M. Haddad and Sergey G. Nersesov
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691153469
- eISBN:
- 9781400842667
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691153469.003.0005
- Subject:
- Mathematics, Applied Mathematics
This chapter introduces the notion of a control vector Lyapunov function as a generalization of control Lyapunov functions, showing that asymptotic stabilizability of a nonlinear dynamical system is ...
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This chapter introduces the notion of a control vector Lyapunov function as a generalization of control Lyapunov functions, showing that asymptotic stabilizability of a nonlinear dynamical system is equivalent to the existence of a control vector Lyapunov function. These control vector Lyapunov functions are used to develop a universal decentralized feedback control law for a decentralized nonlinear dynamical system that possesses guaranteed gain and sector margins in each decentralized input channel. The chapter also describes the connections between the notion of vector dissipativity and optimality of the proposed decentralized feedback control law. The proposed control framework is then used to construct decentralized controllers for large-scale nonlinear dynamical systems with robustness guarantees against full modeling uncertainty.Less
This chapter introduces the notion of a control vector Lyapunov function as a generalization of control Lyapunov functions, showing that asymptotic stabilizability of a nonlinear dynamical system is equivalent to the existence of a control vector Lyapunov function. These control vector Lyapunov functions are used to develop a universal decentralized feedback control law for a decentralized nonlinear dynamical system that possesses guaranteed gain and sector margins in each decentralized input channel. The chapter also describes the connections between the notion of vector dissipativity and optimality of the proposed decentralized feedback control law. The proposed control framework is then used to construct decentralized controllers for large-scale nonlinear dynamical systems with robustness guarantees against full modeling uncertainty.
Reza Shadmehr and Sandro Mussa-Ivaldi
- Published in print:
- 2012
- Published Online:
- August 2013
- ISBN:
- 9780262016964
- eISBN:
- 9780262301282
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262016964.003.0013
- Subject:
- Neuroscience, Research and Theory
This chapter explains feedback-dependent motor control. It presents examples that support the idea that the motor commands that move the body rely on internal predictions regarding the state of the ...
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This chapter explains feedback-dependent motor control. It presents examples that support the idea that the motor commands that move the body rely on internal predictions regarding the state of the body/environment, and sensory observations. It shows that during a movement, the motor commands depend on the state of the body part that is being controlled, as well as the overall goal of the task. This chapter suggests that the motor commands to control eye and head movements during head-free gaze changes respond to sensory feedback, and the conditions are simulated in which the head is perturbed, demonstrating how it affects the ongoing saccade of the eye.Less
This chapter explains feedback-dependent motor control. It presents examples that support the idea that the motor commands that move the body rely on internal predictions regarding the state of the body/environment, and sensory observations. It shows that during a movement, the motor commands depend on the state of the body part that is being controlled, as well as the overall goal of the task. This chapter suggests that the motor commands to control eye and head movements during head-free gaze changes respond to sensory feedback, and the conditions are simulated in which the head is perturbed, demonstrating how it affects the ongoing saccade of the eye.
Mads Kærn and Ron Weiss
- Published in print:
- 2006
- Published Online:
- August 2013
- ISBN:
- 9780262195485
- eISBN:
- 9780262257060
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262195485.003.0013
- Subject:
- Mathematics, Mathematical Biology
This chapter focuses on the mathematical design and experimental implementation of selected synthetic gene regulatory networks that embody important architectural properties. The chapter is organized ...
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This chapter focuses on the mathematical design and experimental implementation of selected synthetic gene regulatory networks that embody important architectural properties. The chapter is organized as follows. Section 13.2 and 13.3 discuss the general aspects of transcriptional regulation and how transcription is modeled, respectively. The remaining sections highlight how synthetic gene regulatory systems have been designed and implemented in the bacterium Escherichia coli based on network models constructed from phenomenological mathematical descriptions of transcriptional regulation. Sections 13.4 and 13.5 discuss linear transcriptional networks and feedforward networks, respectively. Section 13.6 provides examples of networks that support bistability and oscillations by incorporating feedback control. These systems demonstrate how some of the principles investigated in Chapter 6 have been used to create living cells with complex dynamical properties.Less
This chapter focuses on the mathematical design and experimental implementation of selected synthetic gene regulatory networks that embody important architectural properties. The chapter is organized as follows. Section 13.2 and 13.3 discuss the general aspects of transcriptional regulation and how transcription is modeled, respectively. The remaining sections highlight how synthetic gene regulatory systems have been designed and implemented in the bacterium Escherichia coli based on network models constructed from phenomenological mathematical descriptions of transcriptional regulation. Sections 13.4 and 13.5 discuss linear transcriptional networks and feedforward networks, respectively. Section 13.6 provides examples of networks that support bistability and oscillations by incorporating feedback control. These systems demonstrate how some of the principles investigated in Chapter 6 have been used to create living cells with complex dynamical properties.
Ivan Herreros
- Published in print:
- 2018
- Published Online:
- June 2018
- ISBN:
- 9780199674923
- eISBN:
- 9780191842702
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780199674923.003.0026
- Subject:
- Neuroscience, Sensory and Motor Systems, Development
This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and ...
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This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.Less
This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.
Pablo A. Iglesias and Brian P. Ingalls (eds)
- Published in print:
- 2009
- Published Online:
- August 2013
- ISBN:
- 9780262013345
- eISBN:
- 9780262258906
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262013345.001.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies
Issues of regulation and control are central to the study of biological and biochemical systems. Thus it is not surprising that the tools of feedback control theory—engineering techniques developed ...
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Issues of regulation and control are central to the study of biological and biochemical systems. Thus it is not surprising that the tools of feedback control theory—engineering techniques developed to design and analyze self-regulating systems—have proven useful in the study of these biological mechanisms. Such interdisciplinary work requires knowledge of the results, tools, and techniques of another discipline, as well as an understanding of the culture of an unfamiliar research community. This book attempts to bridge the gap between disciplines by presenting applications of systems and control theory to cell biology that range from surveys of established material to descriptions of new developments in the field. The first chapter offers a primer on concepts from dynamical systems and control theory, which allows the life scientist with no background in control theory to understand the concepts presented in the rest of the book. Following the introduction of ordinary differential equation-based modeling in the first chapter, the second and third chapters discuss alternative modeling frameworks. The remaining chapters sample a variety of applications, considering such topics as quantitative measures of dynamic behavior, modularity, stoichiometry, robust control techniques, and network identification.Less
Issues of regulation and control are central to the study of biological and biochemical systems. Thus it is not surprising that the tools of feedback control theory—engineering techniques developed to design and analyze self-regulating systems—have proven useful in the study of these biological mechanisms. Such interdisciplinary work requires knowledge of the results, tools, and techniques of another discipline, as well as an understanding of the culture of an unfamiliar research community. This book attempts to bridge the gap between disciplines by presenting applications of systems and control theory to cell biology that range from surveys of established material to descriptions of new developments in the field. The first chapter offers a primer on concepts from dynamical systems and control theory, which allows the life scientist with no background in control theory to understand the concepts presented in the rest of the book. Following the introduction of ordinary differential equation-based modeling in the first chapter, the second and third chapters discuss alternative modeling frameworks. The remaining chapters sample a variety of applications, considering such topics as quantitative measures of dynamic behavior, modularity, stoichiometry, robust control techniques, and network identification.
Dana H. Ballard
- Published in print:
- 2015
- Published Online:
- September 2015
- ISBN:
- 9780262028615
- eISBN:
- 9780262323819
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262028615.003.0007
- Subject:
- Neuroscience, Research and Theory
The complexities of directing complicated high-degree of freedom interactions in the Newtonian world make the human motor system the most complicated of the brain’s large-scale systems. In particular ...
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The complexities of directing complicated high-degree of freedom interactions in the Newtonian world make the human motor system the most complicated of the brain’s large-scale systems. In particular this system exhibits separate subsystems for planning, balance, and progress-monitoring, as well as vast libraries of complex movement primitives built into the spinal cord. Roughly the subsystems can be thought of as being divided into categories of fast (less than 100 milliseconds) and slow (more than 100 milliseconds). The slow subsystems are in the forebrain. The Cortex solves the formidable problem of translating the goals of a movement described in world coordinates into a posture change. This change must also include stiffness parameters, which specify how the body will react upon contacting surfaces. Posture changes are coded to reduce bandwidth, which allows them to be carried out in a timely manner by Spinal Cord circuitry, which adds essential load balancing functionality.Less
The complexities of directing complicated high-degree of freedom interactions in the Newtonian world make the human motor system the most complicated of the brain’s large-scale systems. In particular this system exhibits separate subsystems for planning, balance, and progress-monitoring, as well as vast libraries of complex movement primitives built into the spinal cord. Roughly the subsystems can be thought of as being divided into categories of fast (less than 100 milliseconds) and slow (more than 100 milliseconds). The slow subsystems are in the forebrain. The Cortex solves the formidable problem of translating the goals of a movement described in world coordinates into a posture change. This change must also include stiffness parameters, which specify how the body will react upon contacting surfaces. Posture changes are coded to reduce bandwidth, which allows them to be carried out in a timely manner by Spinal Cord circuitry, which adds essential load balancing functionality.
Nico Orlandi and Geoff Lee
- Published in print:
- 2019
- Published Online:
- May 2019
- ISBN:
- 9780190662813
- eISBN:
- 9780190662844
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190662813.003.0016
- Subject:
- Philosophy, Philosophy of Mind
This chapter discusses Andy Clark’s recent explorations of Bayesian perceptual models and predictive processing. In the first part, the chapter discusses the predictive processing (PP) framework, ...
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This chapter discusses Andy Clark’s recent explorations of Bayesian perceptual models and predictive processing. In the first part, the chapter discusses the predictive processing (PP) framework, explicating its relationship with hierarchical Bayesian models in theories of perception. In the second part, it examines the relationship between perception and action in the PP model. The overarching goal is twofold: first, to get clearer on the picture of mental activity that Clark is presenting, including what exactly is represented at the levels of the perception/action hierarchy, and the nature of the information processing it postulates; second, although the framework presented by Clark certainly has interesting novel features, some of his glosses on it are misleading. In particular, Clark’s interpretation of predictive processing as essentially a top-down, expectation-driven process, on which perception is aptly thought of as “controlled hallucination,” exaggerates the contrast with the traditional picture of perception as bottom-up and stimulus-driven. Additionally, despite the rhetoric, Clark’s PP model substantially preserves the traditional distinction between perception and action.Less
This chapter discusses Andy Clark’s recent explorations of Bayesian perceptual models and predictive processing. In the first part, the chapter discusses the predictive processing (PP) framework, explicating its relationship with hierarchical Bayesian models in theories of perception. In the second part, it examines the relationship between perception and action in the PP model. The overarching goal is twofold: first, to get clearer on the picture of mental activity that Clark is presenting, including what exactly is represented at the levels of the perception/action hierarchy, and the nature of the information processing it postulates; second, although the framework presented by Clark certainly has interesting novel features, some of his glosses on it are misleading. In particular, Clark’s interpretation of predictive processing as essentially a top-down, expectation-driven process, on which perception is aptly thought of as “controlled hallucination,” exaggerates the contrast with the traditional picture of perception as bottom-up and stimulus-driven. Additionally, despite the rhetoric, Clark’s PP model substantially preserves the traditional distinction between perception and action.
Pierre-Loïc Garoche
- Published in print:
- 2019
- Published Online:
- January 2020
- ISBN:
- 9780691181301
- eISBN:
- 9780691189581
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691181301.003.0004
- Subject:
- Mathematics, Applied Mathematics
This chapter presents the formalisms describing discrete dynamical systems and gives an overview on the convex optimization tools and methods used to compute the analyses. A dynamical system is a ...
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This chapter presents the formalisms describing discrete dynamical systems and gives an overview on the convex optimization tools and methods used to compute the analyses. A dynamical system is a typical object used in control systems or in signal processing. In some cases, it is eventually implemented in a program to perform the desired feedback control to a cyber-physical system. Language-wise, model-based languages such as LUSTRE, ANSYS SCADE, or MATLAB Simulink provide primitives to build these dynamical systems or controllers relying on simpler constructs. In terms of programs, such dynamical systems can easily be implemented as a “while true loop” initialized by the initial state and performing the update f. The simplest systems are usually directly coded in the target language, while more advanced systems are compiled through autocoders.Less
This chapter presents the formalisms describing discrete dynamical systems and gives an overview on the convex optimization tools and methods used to compute the analyses. A dynamical system is a typical object used in control systems or in signal processing. In some cases, it is eventually implemented in a program to perform the desired feedback control to a cyber-physical system. Language-wise, model-based languages such as LUSTRE, ANSYS SCADE, or MATLAB Simulink provide primitives to build these dynamical systems or controllers relying on simpler constructs. In terms of programs, such dynamical systems can easily be implemented as a “while true loop” initialized by the initial state and performing the update f. The simplest systems are usually directly coded in the target language, while more advanced systems are compiled through autocoders.
