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  • 1
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    Object decoding with attention in inferior temporal cortex [Neuroscience] (2011)
    National Academy of Sciences
    Details
    Publication Date: 2011-05-25
    Description: Recognizing objects in cluttered scenes requires attentional mechanisms to filter out distracting information. Previous studies have found several physiological correlates of attention in visual cortex, including larger responses for attended objects. However, it has been unclear whether these attention-related changes have a large impact on information about objects at the neural population level. To address this question, we trained monkeys to covertly deploy their visual attention from a central fixation point to one of three objects displayed in the periphery, and we decoded information about the identity and position of the objects from populations of ∼200 neurons from the inferior temporal cortex using a pattern classifier. The results show that before attention was deployed, information about the identity and position of each object was greatly reduced relative to when these objects were shown in isolation. However, when a monkey attended to an object, the pattern of neural activity, represented as a vector with dimensionality equal to the size of the neural population, was restored toward the vector representing the isolated object. Despite this nearly exclusive representation of the attended object, an increase in the salience of nonattended objects caused “bottom-up” mechanisms to override these “top-down” attentional enhancements. The method described here can be used to assess which attention-related physiological changes are directly related to object recognition, and should be helpful in assessing the role of additional physiological changes in the future.
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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  • 2
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    Categorical representation of visual stimuli in the primate prefrontal cortex (2001)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2001-02-24
    Description: The ability to group stimuli into meaningful categories is a fundamental cognitive process. To explore its neural basis, we trained monkeys to categorize computer-generated stimuli as "cats" and "dogs." A morphing system was used to systematically vary stimulus shape and precisely define the category boundary. Neural activity in the lateral prefrontal cortex reflected the category of visual stimuli, even when a monkey was retrained with the stimuli assigned to new categories.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Freedman, D J -- Riesenhuber, M -- Poggio, T -- Miller, E K -- New York, N.Y. -- Science. 2001 Jan 12;291(5502):312-6.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Center for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/11209083" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Brain Mapping ; Cats ; Cognition ; Dogs ; Form Perception ; Haplorhini ; Learning ; Mental Processes/*physiology ; Neurons/*physiology ; Photic Stimulation ; Prefrontal Cortex/*physiology ; Temporal Lobe/physiology
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 3
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    Regularization algorithms for learning that are equivalent to multilayer networks (1990)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 1990-02-23
    Description: Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensional function (that is, solving the problem of hypersurface reconstruction). From this point of view, this form of learning is closely related to classical approximation techniques, such as generalized splines and regularization theory. A theory is reported that shows the equivalence between regularization and a class of three-layer networks called regularization networks or hyper basis functions. These networks are not only equivalent to generalized splines but are also closely related to the classical radial basis functions used for interpolation tasks and to several pattern recognition and neural network algorithms. They also have an interesting interpretation in terms of prototypes that are synthesized and optimally combined during the learning stage.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Poggio, T -- Girosi, F -- New York, N.Y. -- Science. 1990 Feb 23;247(4945):978-82.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/17776454" target="_blank"〉PubMed〈/a〉
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 4
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    Fast perceptual learning in visual hyperacuity (1992)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 1992-05-15
    Description: In many different spatial discrimination tasks, such as in determining the sign of the offset in a vernier stimulus, the human visual system exhibits hyperacuity by evaluating spatial relations with the precision of a fraction of a photoreceptor's diameter. It is proposed that this impressive performance depends in part on a fast learning process that uses relatively few examples and that occurs at an early processing stage in the visual pathway. This hypothesis is given support by the demonstration that it is possible to synthesize, from a small number of examples of a given task, a simple network that attains the required performance level. Psychophysical experiments agree with some of the key predictions of the model. In particular, fast stimulus-specific learning is found to take place in the human visual system, and this learning does not transfer between two slightly different hyperacuity tasks.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Poggio, T -- Fahle, M -- Edelman, S -- New York, N.Y. -- Science. 1992 May 15;256(5059):1018-21.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge 02139.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/1589770" target="_blank"〉PubMed〈/a〉
    Keywords: Algorithms ; Computer Simulation ; Humans ; Kinetics ; Learning/*physiology ; Models, Biological ; Photoreceptor Cells/physiology ; Visual Acuity/*physiology ; Visual Pathways/physiology ; Visual Perception/*physiology
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 5
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    Fast readout of object identity from macaque inferior temporal cortex (2005)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2005-11-08
    Description: Understanding the brain computations leading to object recognition requires quantitative characterization of the information represented in inferior temporal (IT) cortex. We used a biologically plausible, classifier-based readout technique to investigate the neural coding of selectivity and invariance at the IT population level. The activity of small neuronal populations (approximately 100 randomly selected cells) over very short time intervals (as small as 12.5 milliseconds) contained unexpectedly accurate and robust information about both object "identity" and "category." This information generalized over a range of object positions and scales, even for novel objects. Coarse information about position and scale could also be read out from the same population.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Hung, Chou P -- Kreiman, Gabriel -- Poggio, Tomaso -- DiCarlo, James J -- New York, N.Y. -- Science. 2005 Nov 4;310(5749):863-6.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉McGovern Institute for Brain Research, Cambridge, MA 02139, USA. chouhung@mit.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/16272124" target="_blank"〉PubMed〈/a〉
    Keywords: Action Potentials ; Animals ; Brain Mapping ; Macaca mulatta ; Neurons/*physiology ; Psychomotor Performance ; *Recognition (Psychology) ; Temporal Lobe/*physiology ; Time Factors ; *Visual Perception
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 6
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    Donald Arthur Glaser (1926-2013) (2013)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2013-04-05
    Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Poggio, Tomaso -- England -- Nature. 2013 Apr 4;496(7443):32. doi: 10.1038/496032a.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. tp@csail.mit.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/23552936" target="_blank"〉PubMed〈/a〉
    Keywords: Biotechnology/*history ; History, 20th Century ; Molecular Biology/history ; Nobel Prize ; Physics/*history ; United States
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Nature Publishing Group (NPG)
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  • 7
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    Image representations for visual learning (1996)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 1996-06-28
    Description: Computer vision researchers are developing new approaches to object recognition and detection that are based almost directly on images and avoid the use of intermediate three-dimensional models. Many of these techniques depend on a representation of images that induce a linear vector space structure and in principle requires dense feature correspondence. This image representation allows the use of learning techniques for the analysis of images (for computer vision) as well as for the synthesis of images (for computer graphics).〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Beymer, D -- Poggio, T -- New York, N.Y. -- Science. 1996 Jun 28;272(5270):1905-9.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Brain and Cognitive Science, Center for Biological and Computational Learning (CBCL) and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge 02142, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/8658162" target="_blank"〉PubMed〈/a〉
    Keywords: *Artificial Intelligence ; *Computer Graphics ; Computer Simulation ; *Image Processing, Computer-Assisted ; Pattern Recognition, Automated
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 8
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    Parallel integration of vision modules (1988)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 1988-10-21
    Description: Computer algorithms have been developed for several early vision processes, such as edge detection, stereopsis, motion, texture, and color, that give separate cues to the distance from the viewer of three-dimensional surfaces, their shape, and their material properties. Not surprisingly, biological vision systems still greatly outperform computer vision programs. One of the keys to the reliability, flexibility, and robustness of biological vision systems is their ability to integrate several visual cues. A computational technique for integrating different visual cues has now been developed and implemented with encouraging results on a parallel supercomputer.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Poggio, T -- Gamble, E B -- Little, J J -- New York, N.Y. -- Science. 1988 Oct 21;242(4877):436-40.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge 02139.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/3175666" target="_blank"〉PubMed〈/a〉
    Keywords: Algorithms ; Color Perception ; Depth Perception ; Humans ; *Models, Biological ; *Models, Psychological ; Motion Perception ; *Vision, Ocular ; *Visual Perception
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 9
    Electronic Resource
    Electronic Resource
    Automatic person recognition by acoustic and geometric features (1995)
    Springer
    Machine vision and applications 8 (1995), S. 317-325 
    Details
    ISSN: 1432-1769
    Keywords: Face recognition ; Speaker identification ; Classification
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes a multisensorial person-identification system in which visual and acoustic cues are used jointly for person identification. A simple approach, based on the fusion of the lists of scores produced independently by a speaker-recognition system and a face-recognition system, is presented. Experiments are reported that show that the integration of visual and acoustic information enhances both the performance and the reliability of the separate systems. Finally, two network architectures, based on radial basis-function theory, are proposed to describe integration at various levels of abstraction.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01211493
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  • 10
    Electronic Resource
    Electronic Resource
    Automatic person recognition by acoustic and geometric features (1995)
    Springer
    Machine vision and applications 8 (1995), S. 317-325 
    Details
    ISSN: 1432-1769
    Keywords: Key words: Face recognition - Speaker identification - Classification
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. This paper describes a multisensorial person-identification system in which visual and acoustic cues are used jointly for person identification. A simple approach, based on the fusion of the lists of scores produced independently by a speaker-recognition system and a face-recognition system, is presented. Experiments are reported that show that the integration of visual and acoustic information enhances both the performance and the reliability of the separate systems. Finally, two network architectures, based on radial basis-function theory, are proposed to describe integration at various levels of abstraction.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01211493
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    Paper (German National Licenses)
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