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.
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.
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.
[2] Horace B. Barlow was one of the first investigators to use the concept of the feature detector to relate the receptive field of a neuron to a specific animal behavior.
[3] In the same year, Stephen Kuffler published in vivo evidence for an excitatory center, inhibitory surround architecture in the ganglion cells of the mammalian retina which further supported Barlow's suggestion that on-off units can code for behaviorally relevant events.
[5] On the other hand, during Barlow's career, Nikolaas Tinbergen was introducing the concept of the innate release mechanism (IRM) and sign stimulus.
[6] In the late 1950s, Jerome Lettvin and his colleagues began to expand the feature detection hypothesis and clarify the relationship between single neurons and sensory perception.
From their discovery of these uniquely orienting receptive fields, Hubel and Wiesel concluded that orientation-selective cells exist within the cat's visual cortex.
Hubel and Wiesel's investigation of the cat visual cortex sparked interest in the feature detection hypothesis and its relevance to other sensory systems.
Using worm and anti-worm stimuli like these, Ewert identified that the prey-recognition system in the visual pathway of the toad is based on a number of innate release mechanisms.
After determining the sensory recognition elements of each behavior with this experimental setup, Ewert and co-workers examined the neural mechanisms governing the toad's prey-recognition system and found a number of feature detectors.
Evidently, these neurons exhibit a preference in responses to the worm configuration of moving bar stimuli and can therefore be considered feature detectors.
To get a general idea of their properties, in successive experiments various rectangular dark objects of different edge lengths traverse a toad's visual field against a bright background at constant velocity; then the discharge frequency of a T5.2 neuron towards such an object is correlated with the toad's promptness of responding with prey-capture, expressed by the response latency.
Multiple unit recordings showed that a prey object activates several adjacent prey-selective neurons whose receptive fields partly overlap.
Further comparisons between the receptive fields of tectal neurons and retinal ganglion cells, classes R2 and R3, recorded in free-moving toads, revealed that size-sensitive (T5.1) and prey-selective (T5.2) tectal neurons were able to estimate the absolute size of a moving stimulus while retinal ganglion cells were only able to determine the visual angular size of the stimulus.
[15] Axons from the feature sensitive/selective neurons of the optic tectum and thalamic-pretectal region then contact motor structures in the medulla oblongata,[18][19] thus forming a sensorimotor interface.
According to Ewert, this sensorimotor interface may serve as the "releaser" which recognizes sensory signals with assemblies of complex feature detectors and executes the corresponding motor responses.
They have a specialized electric sense made up of tuberous and ampullary electroreceptors located over the skin surface and innervated by the electrosensory lateral line.
These pathways converge in the medial geniculate body—giving rise to more complex feature detectors that respond to specific combinations of CF and FM signals.
In FM-FM regions of the auditory cortex, Suga et al. (1993) identified combination-sensitive neurons which receive inputs from multiple sources.
These FM-FM neurons can be considered complex feature detectors because they are sensitive to a particular frequency combination and a specific time delay between the echo and call.
