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  • 1
    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
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  • 2
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    American Association for the Advancement of Science (AAAS)
    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
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