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9.71 Functional MRI of High-Level Vision (MIT) 9.71 Functional MRI of High-Level Vision (MIT)

Description

Fundamental questions about the human brain can now be answered using straightforward applications of fMRI. This is particularly true in the area of high-level vision, the study of how we interpret and use visual information (including object recognition, visual attention, perceptual awareness, visually guided action, visual memory, and other topics). Students will read, present to the class, and critique current neuroimaging articles, as well as write detailed proposals for experiments of their own.This course covers the basics of fMRI, the strengths and limitations of fMRI compared to other techniques, and the design and analysis of fMRI experiments, focusing primarily on experiments on high-level vision. Upon completion, students should be able to understand and critique published fMRI Fundamental questions about the human brain can now be answered using straightforward applications of fMRI. This is particularly true in the area of high-level vision, the study of how we interpret and use visual information (including object recognition, visual attention, perceptual awareness, visually guided action, visual memory, and other topics). Students will read, present to the class, and critique current neuroimaging articles, as well as write detailed proposals for experiments of their own.This course covers the basics of fMRI, the strengths and limitations of fMRI compared to other techniques, and the design and analysis of fMRI experiments, focusing primarily on experiments on high-level vision. Upon completion, students should be able to understand and critique published fMRI

Subjects

functional magnetic resonance imaging (fMRI) | functional magnetic resonance imaging (fMRI) | neural activity | neural activity | human | human | brain | brain | noninvasive | noninvasive | resolution | resolution | high-level vision | high-level vision | object recognition | object recognition | visual attention | visual attention | perceptual awareness | perceptual awareness | visually guided action | visually guided action | visual memory | visual memory

License

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9.913-C Pattern Recognition for Machine Vision (MIT) 9.913-C Pattern Recognition for Machine Vision (MIT)

Description

The course is directed towards advanced undergraduate and beginning graduate students. It will focus on applications of pattern recognition techniques to problems of machine vision.The topics covered in the course include:Overview of problems of machine vision and pattern classificationImage formation and processingFeature extraction from imagesBiological object recognitionBayesian decision theoryClustering The course is directed towards advanced undergraduate and beginning graduate students. It will focus on applications of pattern recognition techniques to problems of machine vision.The topics covered in the course include:Overview of problems of machine vision and pattern classificationImage formation and processingFeature extraction from imagesBiological object recognitionBayesian decision theoryClustering

Subjects

pattern recognition | pattern recognition | machine vision | machine vision | pattern classification | pattern classification | Image formation | Image formation | processing | processing | feature extraction | feature extraction | Biological object recognition | Biological object recognition | Bayesian Decision Theory | Bayesian Decision Theory | Clustering | Clustering

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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9.71 Functional MRI of High-Level Vision (MIT) 9.71 Functional MRI of High-Level Vision (MIT)

Description

This course covers the basics of fMRI, the strengths and limitations of fMRI compared to other techniques, and the design and analysis of fMRI experiments, focusing primarily on experiments on high-level vision. Upon completion, students should be able to understand and critique published fMRI papers, have a good grasp on what is known about high-level vision from fMRI, and design their own fMRI experiments. This course covers the basics of fMRI, the strengths and limitations of fMRI compared to other techniques, and the design and analysis of fMRI experiments, focusing primarily on experiments on high-level vision. Upon completion, students should be able to understand and critique published fMRI papers, have a good grasp on what is known about high-level vision from fMRI, and design their own fMRI experiments.

Subjects

functional magnetic resonance imaging (fMRI) | functional magnetic resonance imaging (fMRI) | neural activity | neural activity | human | human | brain | brain | noninvasive | noninvasive | resolution | resolution | high-level vision | high-level vision | object recognition | object recognition | visual attention | visual attention | perceptual awareness | perceptual awareness | visually guided action | visually guided action | visual memory | visual memory

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.803 The Human Intelligence Enterprise (MIT) 6.803 The Human Intelligence Enterprise (MIT)

Description

6.803/6.833 is a course in the department's "Artifical Intelligence and Applications" concentration. This course is offered both to undergraduates (6.803) and graduates (6.833). 6.803/6.833 is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course. 6.803/6.833 is a course in the department's "Artifical Intelligence and Applications" concentration. This course is offered both to undergraduates (6.803) and graduates (6.833). 6.803/6.833 is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Subjects

Human Intelligence Enterprise | Human Intelligence Enterprise | artificial intelligence | artificial intelligence | computational models | computational models | perception | perception | cognition | cognition | neuroscience | neuroscience | human behavior | human behavior | communication | communication | heuristics | heuristics | object tracking | object tracking | object recognition | object recognition | change representation | change representation | language evolution | language evolution | Turing | Turing | Minsky | Minsky

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.803 The Human Intelligence Enterprise (MIT) 6.803 The Human Intelligence Enterprise (MIT)

Description

This course is offered both to undergraduates (6.803) and graduates (6.833) and is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because it focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course. This course is offered both to undergraduates (6.803) and graduates (6.833) and is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because it focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Subjects

Human Intelligence Enterprise | Human Intelligence Enterprise | artificial intelligence | artificial intelligence | computational models | computational models | perception | perception | cognition | cognition | neuroscience | neuroscience | human behavior | human behavior | communication | communication | heuristics | heuristics | object tracking | object tracking | object recognition | object recognition | change representation | change representation | language evolution | language evolution | Turing | Turing | Minsky | Minsky

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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9.71 Functional MRI of High-Level Vision (MIT) 9.71 Functional MRI of High-Level Vision (MIT)

Description

We are now at an unprecedented point in the field of neuroscience: We can watch the human brain in action as it sees, thinks, decides, reads, and remembers. Functional magnetic resonance imaging (fMRI) is the only method that enables us to monitor local neural activity in the normal human brain in a noninvasive fashion and with good spatial resolution. A large number of far-reaching and fundamental questions about the human mind and brain can now be answered using straightforward applications of this technology. This is particularly true in the area of high-level vision, the study of how we interpret and use visual information including object recognition, mental imagery, visual attention, perceptual awareness, visually guided action, and visual memory. The goals of this course are to help We are now at an unprecedented point in the field of neuroscience: We can watch the human brain in action as it sees, thinks, decides, reads, and remembers. Functional magnetic resonance imaging (fMRI) is the only method that enables us to monitor local neural activity in the normal human brain in a noninvasive fashion and with good spatial resolution. A large number of far-reaching and fundamental questions about the human mind and brain can now be answered using straightforward applications of this technology. This is particularly true in the area of high-level vision, the study of how we interpret and use visual information including object recognition, mental imagery, visual attention, perceptual awareness, visually guided action, and visual memory. The goals of this course are to help

Subjects

functional magnetic resonance imaging (fMRI) | functional magnetic resonance imaging (fMRI) | neural activity | neural activity | human | human | brain | brain | noninvasive | noninvasive | resolution | resolution | high-level vision | high-level vision | object recognition | object recognition | visual attention | visual attention | perceptual awareness | perceptual awareness | visually guided action | visually guided action | visual memory | visual memory | voxelwise analysis | voxelwise analysis | conjugate mirroring | conjugate mirroring | interleaved stimulus presentation | interleaved stimulus presentation | magnetization following excitation | magnetization following excitation | active voxels | active voxels | scanner drift | scanner drift | trial sorting | trial sorting | collinear factors | collinear factors | different model factors | different model factors | mock scanner | mock scanner | scanner session | scanner session | visual stimulation task | visual stimulation task | hemoglobin signal | hemoglobin signal | labeling plane | labeling plane | nearby voxels | nearby voxels | shimming coils | shimming coils | bias field estimation | bias field estimation | conscious encoding | conscious encoding | spiral imaging | spiral imaging | functional resolution | functional resolution | hemodynamic activity | hemodynamic activity | direct cortical stimulation | direct cortical stimulation | physiological noise | physiological noise | refractory effects | refractory effects | independent statistical tests. | independent statistical tests.

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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9.459 Scene Understanding Symposium (MIT) 9.459 Scene Understanding Symposium (MIT)

Description

What are the circuits, mechanisms and representations that permit the recognition of a visual scene from just one glance? In this one-day seminar on Scene Understanding, speakers from a variety of disciplines - neurophysiology, cognitive neuroscience, visual cognition, computational neuroscience and computer vision - will address a range of topics related to scene recognition, including natural image categorization, contextual effects on object recognition, and the role of attention in scene understanding and visual art. The goal is to encourage exchanges between researchers of all fields of brain sciences in the burgeoning field of scene understanding. What are the circuits, mechanisms and representations that permit the recognition of a visual scene from just one glance? In this one-day seminar on Scene Understanding, speakers from a variety of disciplines - neurophysiology, cognitive neuroscience, visual cognition, computational neuroscience and computer vision - will address a range of topics related to scene recognition, including natural image categorization, contextual effects on object recognition, and the role of attention in scene understanding and visual art. The goal is to encourage exchanges between researchers of all fields of brain sciences in the burgeoning field of scene understanding.

Subjects

circuits | mechanisms and representation | circuits | mechanisms and representation | recognition of a visual scene | recognition of a visual scene | Scene Understanding | Scene Understanding | neurophysiology | neurophysiology | cognitive neuroscience | cognitive neuroscience | visual cognition | visual cognition | computational neuroscience | computational neuroscience | computer vision | computer vision | natural image categorization | natural image categorization | contextual effects on object recognition | contextual effects on object recognition | role of attention in scene understanding | role of attention in scene understanding

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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9.916 The Neural Basis of Visual Object Recognition in Monkeys and Humans (MIT) 9.916 The Neural Basis of Visual Object Recognition in Monkeys and Humans (MIT)

Description

Understanding the brain's remarkable ability for visual object recognition is one of the greatest challenges of brain research. The goal of this course is to provide an overview of key issues of object representation and to survey data from primate physiology and human fMRI that bear on those issues. Topics include the computational problems of object representation, the nature of object representations in the brain, the tolerance and selectivity of those representations, and the effects of attention and learning. Understanding the brain's remarkable ability for visual object recognition is one of the greatest challenges of brain research. The goal of this course is to provide an overview of key issues of object representation and to survey data from primate physiology and human fMRI that bear on those issues. Topics include the computational problems of object representation, the nature of object representations in the brain, the tolerance and selectivity of those representations, and the effects of attention and learning.

Subjects

vision | vision | object recognition | object recognition | monkey versus human | monkey versus human | object representations | object representations | fMRI | fMRI | temporal lobe | temporal lobe | visual cortex | visual cortex | neuronal representations | neuronal representations | neurophysiology | neurophysiology | retinal image | retinal image | pattern recognition | pattern recognition | perceptual awareness | perceptual awareness

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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9.65 Cognitive Processes (MIT) 9.65 Cognitive Processes (MIT)

Description

This undergraduate course is designed to introduce students to cognitive processes. The broad range of topics covers each of the areas in the field of cognition, and presents the current thinking in this discipline. As an introduction to human information processing and learning, the topics include the nature of mental representation and processing, the architecture of memory, pattern recognition, attention, imagery and mental codes, concepts and prototypes, reasoning and problem solving. This undergraduate course is designed to introduce students to cognitive processes. The broad range of topics covers each of the areas in the field of cognition, and presents the current thinking in this discipline. As an introduction to human information processing and learning, the topics include the nature of mental representation and processing, the architecture of memory, pattern recognition, attention, imagery and mental codes, concepts and prototypes, reasoning and problem solving.

Subjects

cognitive science | cognitive science | cognitive processes | cognitive processes | cognition | cognition | the mind | the mind | object recognition | object recognition | attention | attention | memory | memory | associative memory | associative memory | learning | learning | implicit memory | implicit memory | conceptual short term memory | conceptual short term memory | working memory | working memory | language | language | concepts | concepts | prototypes | prototypes | psycholinguistics | psycholinguistics | visual knowledge | visual knowledge | mental codes | mental codes | judgement | judgement | reasoning | reasoning | problem-solving | problem-solving | conscious thought | conscious thought | unconscious thought | unconscious thought

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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9.67 Object and Face Recognition (MIT) 9.67 Object and Face Recognition (MIT)

Description

Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems. Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.

Subjects

object recognition | object recognition | neural | neural | computation | computation | representation | representation | 3-D objects | 3-D objects | 2-D images | 2-D images | face recognition | face recognition | human face recognition | human face recognition | artificial computational systems | artificial computational systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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9.67 Object and Face Recognition (MIT) 9.67 Object and Face Recognition (MIT)

Description

Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems. Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.

Subjects

object recognition | object recognition | neural | neural | computation | computation | representation | representation | 3-D objects | 3-D objects | 2-D images | 2-D images | face recognition | face recognition | human face recognition | human face recognition | artificial computational systems | artificial computational systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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9.67 Object and Face Recognition (MIT)

Description

Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.

Subjects

object recognition | neural | computation | representation | 3-D objects | 2-D images | face recognition | human face recognition | artificial computational systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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9.65 Cognitive Processes (MIT)

Description

This undergraduate course is designed to introduce students to cognitive processes. The broad range of topics covers each of the areas in the field of cognition, and presents the current thinking in this discipline. As an introduction to human information processing and learning, the topics include the nature of mental representation and processing, the architecture of memory, pattern recognition, attention, imagery and mental codes, concepts and prototypes, reasoning and problem solving.

Subjects

cognitive science | cognitive processes | cognition | the mind | object recognition | attention | memory | associative memory | learning | implicit memory | conceptual short term memory | working memory | language | concepts | prototypes | psycholinguistics | visual knowledge | mental codes | judgement | reasoning | problem-solving | conscious thought | unconscious thought

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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9.67 Object and Face Recognition (MIT)

Description

Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.

Subjects

object recognition | neural | computation | representation | 3-D objects | 2-D images | face recognition | human face recognition | artificial computational systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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9.67 Object and Face Recognition (MIT)

Description

Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.

Subjects

object recognition | neural | computation | representation | 3-D objects | 2-D images | face recognition | human face recognition | artificial computational systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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9.71 Functional MRI of High-Level Vision (MIT)

Description

Fundamental questions about the human brain can now be answered using straightforward applications of fMRI. This is particularly true in the area of high-level vision, the study of how we interpret and use visual information (including object recognition, visual attention, perceptual awareness, visually guided action, visual memory, and other topics). Students will read, present to the class, and critique current neuroimaging articles, as well as write detailed proposals for experiments of their own.This course covers the basics of fMRI, the strengths and limitations of fMRI compared to other techniques, and the design and analysis of fMRI experiments, focusing primarily on experiments on high-level vision. Upon completion, students should be able to understand and critique published fMRI

Subjects

functional magnetic resonance imaging (fMRI) | neural activity | human | brain | noninvasive | resolution | high-level vision | object recognition | visual attention | perceptual awareness | visually guided action | visual memory

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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9.913-C Pattern Recognition for Machine Vision (MIT)

Description

The course is directed towards advanced undergraduate and beginning graduate students. It will focus on applications of pattern recognition techniques to problems of machine vision.The topics covered in the course include:Overview of problems of machine vision and pattern classificationImage formation and processingFeature extraction from imagesBiological object recognitionBayesian decision theoryClustering

Subjects

pattern recognition | machine vision | pattern classification | Image formation | processing | feature extraction | Biological object recognition | Bayesian Decision Theory | Clustering

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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9.71 Functional MRI of High-Level Vision (MIT)

Description

This course covers the basics of fMRI, the strengths and limitations of fMRI compared to other techniques, and the design and analysis of fMRI experiments, focusing primarily on experiments on high-level vision. Upon completion, students should be able to understand and critique published fMRI papers, have a good grasp on what is known about high-level vision from fMRI, and design their own fMRI experiments.

Subjects

functional magnetic resonance imaging (fMRI) | neural activity | human | brain | noninvasive | resolution | high-level vision | object recognition | visual attention | perceptual awareness | visually guided action | visual memory

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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9.71 Functional MRI of High-Level Vision (MIT)

Description

We are now at an unprecedented point in the field of neuroscience: We can watch the human brain in action as it sees, thinks, decides, reads, and remembers. Functional magnetic resonance imaging (fMRI) is the only method that enables us to monitor local neural activity in the normal human brain in a noninvasive fashion and with good spatial resolution. A large number of far-reaching and fundamental questions about the human mind and brain can now be answered using straightforward applications of this technology. This is particularly true in the area of high-level vision, the study of how we interpret and use visual information including object recognition, mental imagery, visual attention, perceptual awareness, visually guided action, and visual memory. The goals of this course are to help

Subjects

functional magnetic resonance imaging (fMRI) | neural activity | human | brain | noninvasive | resolution | high-level vision | object recognition | visual attention | perceptual awareness | visually guided action | visual memory | voxelwise analysis | conjugate mirroring | interleaved stimulus presentation | magnetization following excitation | active voxels | scanner drift | trial sorting | collinear factors | different model factors | mock scanner | scanner session | visual stimulation task | hemoglobin signal | labeling plane | nearby voxels | shimming coils | bias field estimation | conscious encoding | spiral imaging | functional resolution | hemodynamic activity | direct cortical stimulation | physiological noise | refractory effects | independent statistical tests.

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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6.803 The Human Intelligence Enterprise (MIT)

Description

6.803/6.833 is a course in the department's "Artifical Intelligence and Applications" concentration. This course is offered both to undergraduates (6.803) and graduates (6.833). 6.803/6.833 is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Subjects

Human Intelligence Enterprise | artificial intelligence | computational models | perception | cognition | neuroscience | human behavior | communication | heuristics | object tracking | object recognition | change representation | language evolution | Turing | Minsky

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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9.459 Scene Understanding Symposium (MIT)

Description

What are the circuits, mechanisms and representations that permit the recognition of a visual scene from just one glance? In this one-day seminar on Scene Understanding, speakers from a variety of disciplines - neurophysiology, cognitive neuroscience, visual cognition, computational neuroscience and computer vision - will address a range of topics related to scene recognition, including natural image categorization, contextual effects on object recognition, and the role of attention in scene understanding and visual art. The goal is to encourage exchanges between researchers of all fields of brain sciences in the burgeoning field of scene understanding.

Subjects

circuits | mechanisms and representation | recognition of a visual scene | Scene Understanding | neurophysiology | cognitive neuroscience | visual cognition | computational neuroscience | computer vision | natural image categorization | contextual effects on object recognition | role of attention in scene understanding

License

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9.916 The Neural Basis of Visual Object Recognition in Monkeys and Humans (MIT)

Description

Understanding the brain's remarkable ability for visual object recognition is one of the greatest challenges of brain research. The goal of this course is to provide an overview of key issues of object representation and to survey data from primate physiology and human fMRI that bear on those issues. Topics include the computational problems of object representation, the nature of object representations in the brain, the tolerance and selectivity of those representations, and the effects of attention and learning.

Subjects

vision | object recognition | monkey versus human | object representations | fMRI | temporal lobe | visual cortex | neuronal representations | neurophysiology | retinal image | pattern recognition | perceptual awareness

License

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9.65 Cognitive Processes (MIT)

Description

This undergraduate course is designed to introduce students to cognitive processes. The broad range of topics covers each of the areas in the field of cognition, and presents the current thinking in this discipline. As an introduction to human information processing and learning, the topics include the nature of mental representation and processing, the architecture of memory, pattern recognition, attention, imagery and mental codes, concepts and prototypes, reasoning and problem solving.

Subjects

cognitive science | cognitive processes | cognition | the mind | object recognition | attention | memory | associative memory | learning | implicit memory | conceptual short term memory | working memory | language | concepts | prototypes | psycholinguistics | visual knowledge | mental codes | judgement | reasoning | problem-solving | conscious thought | unconscious thought

License

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9.67 Object and Face Recognition (MIT)

Description

Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.

Subjects

object recognition | neural | computation | representation | 3-D objects | 2-D images | face recognition | human face recognition | artificial computational systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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6.803 The Human Intelligence Enterprise (MIT)

Description

This course is offered both to undergraduates (6.803) and graduates (6.833) and is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because it focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Subjects

Human Intelligence Enterprise | artificial intelligence | computational models | perception | cognition | neuroscience | human behavior | communication | heuristics | object tracking | object recognition | change representation | language evolution | Turing | Minsky

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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