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Feature detection is a process by which the nervous system sorts or filters complex natural stimuli in order to extract behaviorally relevant cues that have a high probability of being associated with important objects or organisms in their environment, as opposed to irrelevant background or noise.
These animal experiments demonstrate that feature detectors seem to be a very primitive development. That is, it did not result from the higher cognitive development of humans. Not surprisingly, there is also evidence that the human brain possesses these elementary feature detectors as well. [5] [6] [7]
Feature integration theory is a theory of attention developed in 1980 by Anne Treisman and Garry Gelade that suggests that when perceiving a stimulus, features are "registered early, automatically, and in parallel, while objects are identified separately" and at a later stage in processing.
Feature detection (nervous system), a biological process for interpreting sensory input Orientation column, also known as a "feature detection column" Feature detection (computer vision), methods for finding parts of an image relevant to a computational task; Feature detection (web development), determining whether a computing environment has ...
The feature integration theory by Anne Treisman is one of the theories that integrates serial and parallel processing while taking into account attentional resources. It consists of two stages- Detection of features-This stage occurs instantaneously and uses parallel processing. In this step, all the basic features of a display are picked up ...
The grandmother cell, sometimes called the "Jennifer Aniston neuron", is a hypothetical neuron that represents a complex but specific concept or object. [1] It activates when a person "sees, hears, or otherwise sensibly discriminates" [2] a specific entity, such as their grandmother.
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The theory of neocortex [5] was primarily motivated by the discoveries of David Hubel and Torsten Wiesel, who found several types of "feature detectors" in the primary visual area of the cortex. Marr proposed, generalising on that observation, that cells in the neocortex are flexible categorizers—that is, they learn the statistical structure ...