Feature detection (nervous system)

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. Feature detectors are individual neurons—or groups of neurons—in the brain which code for perceptually significant stimuli. Early in the sensory pathway feature detectors tend to have simple properties; later they become more and more complex as the features to which they respond become more and more specific. For example, simple cells in the visual cortex of the domestic cat (Felis catus), respond to edges—a feature which is more likely to occur in objects and organisms in the environment. By contrast, the background of a natural visual environment tends to be noisy—emphasizing high spatial frequencies but lacking in extended edges. Responding selectively to an extended edge—either a bright line on a dark background, or the reverse—highlights objects that are near or very large. Edge detectors are useful to a cat, because edges do not occur often in the background "noise" of the visual environment, which is of little consequence to the animal. Early in the history of sensory neurobiology, physiologists favored the idea that the nervous system detected specific features of stimuli, rather than faithfully copying the sensory world onto a sensory map in the brain. For example, they favored the idea that the visual system detects specific features of the visual world. This view contrasted with the metaphor that the retina acts like a camera and the brain acts like film that preserves all elements without making assumptions about what is important in the environment. It wasn't until the late 1950s that the feature detector hypothesis fully developed, and over the last fifty years, it has been the driving force behind most work on sensory systems. Horace B.