Visual cortex and deep networks :: learning invariant representations /
A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a serie...
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| Hauptverfasser: | , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Massachusetts :
The MIT Press,
[2016]
|
| Schriftenreihe: | Computational neuroscience.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks--which do not reflect several important features of the ventral stream architecture and physiology--have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried out in the ventral stream. This book develops a mathematical framework that describes learning of invariant representations of the ventral stream and is particularly relevant to deep convolutional learning networks. The authors propose a theory based on the hypothesis that the main computational goal of the ventral stream is to compute neural representations of images that are invariant to transformations commonly encountered in the visual environment and are learned from unsupervised experience. They describe a general theoretical framework of a computational theory of invariance (with details and proofs offered in appendixes) and then review the application of the theory to the feedforward path of the ventral stream in the primate visual cortex. |
| Beschreibung: | 1 online resource (xiv, 118 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780262336710 0262336715 9780262336703 0262336707 |
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| 520 | |a A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks--which do not reflect several important features of the ventral stream architecture and physiology--have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried out in the ventral stream. This book develops a mathematical framework that describes learning of invariant representations of the ventral stream and is particularly relevant to deep convolutional learning networks. The authors propose a theory based on the hypothesis that the main computational goal of the ventral stream is to compute neural representations of images that are invariant to transformations commonly encountered in the visual environment and are learned from unsupervised experience. They describe a general theoretical framework of a computational theory of invariance (with details and proofs offered in appendixes) and then review the application of the theory to the feedforward path of the ventral stream in the primate visual cortex. | ||
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| series2 | Computational neuroscience |
| spelling | Poggio, Tomaso, author. http://id.loc.gov/authorities/names/n80083007 Visual cortex and deep networks : learning invariant representations / Tomaso A. Poggio, Fabio Anselmi. Cambridge, Massachusetts : The MIT Press, [2016] ©2016 1 online resource (xiv, 118 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Computational neuroscience Includes bibliographical references and index. Print version record. A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks--which do not reflect several important features of the ventral stream architecture and physiology--have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried out in the ventral stream. This book develops a mathematical framework that describes learning of invariant representations of the ventral stream and is particularly relevant to deep convolutional learning networks. The authors propose a theory based on the hypothesis that the main computational goal of the ventral stream is to compute neural representations of images that are invariant to transformations commonly encountered in the visual environment and are learned from unsupervised experience. They describe a general theoretical framework of a computational theory of invariance (with details and proofs offered in appendixes) and then review the application of the theory to the feedforward path of the ventral stream in the primate visual cortex. Visual cortex. http://id.loc.gov/authorities/subjects/sh85143918 Vision. http://id.loc.gov/authorities/subjects/sh85143872 Neural networks (Neurobiology) http://id.loc.gov/authorities/subjects/sh93002348 Perceptual learning. http://id.loc.gov/authorities/subjects/sh85099716 Computational neuroscience. http://id.loc.gov/authorities/subjects/sh97006370 Visual Cortex https://id.nlm.nih.gov/mesh/D014793 Vision, Ocular https://id.nlm.nih.gov/mesh/D014785 Cortex visuel. Vision. Réseaux neuronaux (Neurobiologie) Apprentissage perceptif. Neurosciences informatiques. sight (sense) aat MEDICAL Physiology. bisacsh SCIENCE Life Sciences Human Anatomy & Physiology. bisacsh Computational neuroscience fast Neural networks (Neurobiology) fast Perceptual learning fast Vision fast Visual cortex fast NEUROSCIENCE/General Anselmi, Fabio, author. http://id.loc.gov/authorities/names/n2011087577 has work: Visual cortex and deep networks (Text) https://id.oclc.org/worldcat/entity/E39PCFXfmxBq3C7GqTH8QHP6Vd https://id.oclc.org/worldcat/ontology/hasWork Print version: Poggio, Tomaso. Visual cortex and deep networks. Cambridge, Massachusetts : The MIT Press, [2016] 9780262034722 (DLC) 2016005774 (OCoLC)945072601 Computational neuroscience. http://id.loc.gov/authorities/names/n86711840 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2147389 Volltext |
| spellingShingle | Poggio, Tomaso Anselmi, Fabio Visual cortex and deep networks : learning invariant representations / Computational neuroscience. Visual cortex. http://id.loc.gov/authorities/subjects/sh85143918 Vision. http://id.loc.gov/authorities/subjects/sh85143872 Neural networks (Neurobiology) http://id.loc.gov/authorities/subjects/sh93002348 Perceptual learning. http://id.loc.gov/authorities/subjects/sh85099716 Computational neuroscience. http://id.loc.gov/authorities/subjects/sh97006370 Visual Cortex https://id.nlm.nih.gov/mesh/D014793 Vision, Ocular https://id.nlm.nih.gov/mesh/D014785 Cortex visuel. Vision. Réseaux neuronaux (Neurobiologie) Apprentissage perceptif. Neurosciences informatiques. sight (sense) aat MEDICAL Physiology. bisacsh SCIENCE Life Sciences Human Anatomy & Physiology. bisacsh Computational neuroscience fast Neural networks (Neurobiology) fast Perceptual learning fast Vision fast Visual cortex fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85143918 http://id.loc.gov/authorities/subjects/sh85143872 http://id.loc.gov/authorities/subjects/sh93002348 http://id.loc.gov/authorities/subjects/sh85099716 http://id.loc.gov/authorities/subjects/sh97006370 https://id.nlm.nih.gov/mesh/D014793 https://id.nlm.nih.gov/mesh/D014785 |
| title | Visual cortex and deep networks : learning invariant representations / |
| title_auth | Visual cortex and deep networks : learning invariant representations / |
| title_exact_search | Visual cortex and deep networks : learning invariant representations / |
| title_full | Visual cortex and deep networks : learning invariant representations / Tomaso A. Poggio, Fabio Anselmi. |
| title_fullStr | Visual cortex and deep networks : learning invariant representations / Tomaso A. Poggio, Fabio Anselmi. |
| title_full_unstemmed | Visual cortex and deep networks : learning invariant representations / Tomaso A. Poggio, Fabio Anselmi. |
| title_short | Visual cortex and deep networks : |
| title_sort | visual cortex and deep networks learning invariant representations |
| title_sub | learning invariant representations / |
| topic | Visual cortex. http://id.loc.gov/authorities/subjects/sh85143918 Vision. http://id.loc.gov/authorities/subjects/sh85143872 Neural networks (Neurobiology) http://id.loc.gov/authorities/subjects/sh93002348 Perceptual learning. http://id.loc.gov/authorities/subjects/sh85099716 Computational neuroscience. http://id.loc.gov/authorities/subjects/sh97006370 Visual Cortex https://id.nlm.nih.gov/mesh/D014793 Vision, Ocular https://id.nlm.nih.gov/mesh/D014785 Cortex visuel. Vision. Réseaux neuronaux (Neurobiologie) Apprentissage perceptif. Neurosciences informatiques. sight (sense) aat MEDICAL Physiology. bisacsh SCIENCE Life Sciences Human Anatomy & Physiology. bisacsh Computational neuroscience fast Neural networks (Neurobiology) fast Perceptual learning fast Vision fast Visual cortex fast |
| topic_facet | Visual cortex. Vision. Neural networks (Neurobiology) Perceptual learning. Computational neuroscience. Visual Cortex Vision, Ocular Cortex visuel. Réseaux neuronaux (Neurobiologie) Apprentissage perceptif. Neurosciences informatiques. sight (sense) MEDICAL Physiology. SCIENCE Life Sciences Human Anatomy & Physiology. Computational neuroscience Perceptual learning Vision Visual cortex |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2147389 |
| work_keys_str_mv | AT poggiotomaso visualcortexanddeepnetworkslearninginvariantrepresentations AT anselmifabio visualcortexanddeepnetworkslearninginvariantrepresentations |