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Receptive field
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==Visual system== {{See also|Visual system}} In the visual system, receptive fields are volumes in [[visual space]]. They are smallest in the [[fovea centralis|fovea]] where they can be a few [[minutes of arc]] like a dot on this page, to the whole page. For example, the receptive field of a single [[photoreceptor cell|photoreceptor]] is a cone-shaped volume comprising all the visual directions in which light will alter the firing of that cell. Its [[Apex (geometry)|apex]] is located in the center of the [[lens (anatomy)|lens]] and its base essentially at [[infinity]] in visual space. Traditionally, visual receptive fields were portrayed in two dimensions (e.g., as circles, squares, or rectangles), but these are simply slices, cut along the screen on which the researcher presented the stimulus, of the volume of space to which a particular cell will respond. In the case of [[binocular neurons]] in the [[visual cortex]], receptive fields do not extend to [[optical infinity]]. Instead, they are restricted to a certain interval of distance from the animal, or from where the eyes are fixating (see [[Panum's area]]). The receptive field is often identified as the region of the [[retina]] where the action of [[light]] alters the firing of the neuron. In retinal ganglion cells (see below), this area of the retina would encompass all the photoreceptors, all the [[rod cell|rod]]s and [[cone cell|cone]]s from one [[eye]] that are connected to this particular ganglion cell via [[Retina bipolar cell|bipolar cell]]s, [[horizontal cell]]s, and [[amacrine cell]]s. In [[binocular neurons]] in the visual cortex, it is necessary to specify the corresponding area in both retinas (one in each eye). Although these can be mapped separately in each retina by shutting one or the other eye, the full influence on the neuron's firing is revealed only when both eyes are open. Hubel and Wiesel <ref>e.g., Hubel, 1963; [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/ Hubel-Wiesel, 1962]</ref> advanced the theory that ''receptive fields of cells at one level of the visual system are formed from input by cells at a lower level of the visual system.'' In this way, small, ''simple receptive fields could be combined to form large, complex receptive fields.'' Later theorists elaborated this simple, hierarchical arrangement by allowing cells at one level of the visual system to be influenced by feedback from higher levels. Receptive fields have been mapped for all levels of the visual system from photoreceptors, to retinal ganglion cells, to lateral geniculate nucleus cells, to visual cortex cells, to extrastriate cortical cells. However, because the activities of neurons at any one location are contingent on the activities of neurons across the whole system, i.e. are contingent on changes in the whole field, it is unclear whether a local description of a particular "receptive field" can be considered a general description, robust to changes in the field as a whole. Studies based on perception do not give the full picture of the understanding of visual phenomena, so the electrophysiological tools must be used, as the retina, after all, is an outgrowth of the brain. In retinal ganglion and V1 cells, the receptive field consists of the center ''and'' surround region. ===Retinal ganglion cells=== [[Image:Receptive field.png|thumb|right|300px|On center and off center [[retinal ganglion cell]]s respond oppositely to light in the center and surround of their receptive fields. A strong response means high frequency firing, a weak response is firing at a low frequency, and no response means no action potential is fired.]] [[Image:Red on centre green off centre.png|thumb|300px|right|A computer emulation of "edge detection" using retinal receptive fields. On-centre and off-centre stimulation is shown in red and green respectively.]] Each ganglion cell or optic nerve fiber bears a receptive field, increasing with intensifying light. In the largest field, the light has to be more intense at the periphery of the field than at the center, showing that some synaptic pathways are more preferred than others. The organization of ganglion cells' receptive fields, composed of inputs from many rods and cones, provides a way of detecting contrast, and is used for [[Edge detection|detecting objects' edges]].<ref>{{Cite book|title=Biological psychology|last=Higgs, Suzanne|others=Cooper, Alison (Senior lecturer in neurobiology), Lee, Jonathan (Neuroscientist), Harris, Mike (Mike G.)|isbn=9780857022622|location=Los Angeles|oclc=898753111|date = 2014-12-19}}</ref>{{Rp|188}} Each receptive field is arranged into a central disk, the "center", and a concentric ring, the "surround", each region responding oppositely to light. For example, light in the centre might increase the firing of a particular ganglion cell, whereas light in the surround would decrease the firing of that cell. Stimulation of the center of an on-center cell's receptive field produces ''[[depolarization]]'' and an increase in the firing of the ganglion cell, stimulation of the [[Surround suppression|surround]] produces a ''[[hyperpolarization (biology)|hyperpolarization]]'' and a decrease in the firing of the cell, and stimulation of both the center and surround produces only a mild response (due to mutual inhibition of center and surround). An off-center cell is stimulated by activation of the surround and inhibited by stimulation of the center (see figure). Photoreceptors that are part of the receptive fields of more than one ganglion cell are able to excite or inhibit [[postsynaptic neuron]]s because they release the [[neurotransmitter]] [[glutamate]] at their [[synapse]]s, which can act to depolarize or to hyperpolarize a cell, depending on whether there is a metabotropic or ionotropic receptor on that cell. The ''center-surround receptive field organization'' allows ganglion cells to transmit information not merely about whether photoreceptor cells are exposed to light, but also about the differences in firing rates of cells in the center and surround. This allows them to transmit information about contrast. The size of the receptive field governs the [[spatial frequency]] of the information: small receptive fields are stimulated by high spatial frequencies, fine detail; large receptive fields are stimulated by low spatial frequencies, coarse detail. Retinal ganglion cell receptive fields convey information about discontinuities in the distribution of light falling on the retina; these often specify the edges of objects. In dark adaptation, the peripheral opposite activity zone becomes inactive, but, since it is a diminishing of inhibition between center and periphery, the active field can actually increase, allowing more area for summation. ===Lateral geniculate nucleus=== {{Main|lateral geniculate nucleus}} Further along in the visual system, groups of ganglion cells form the receptive fields of cells in the [[lateral geniculate nucleus]]. Receptive fields are similar to those of ganglion cells, with an antagonistic center-surround system and cells that are either on- or off center. ===Visual cortex=== {{Main|visual cortex}} Receptive fields of cells in the visual cortex are larger and have more-complex stimulus requirements than retinal ganglion cells or lateral geniculate nucleus cells. [[David H. Hubel|Hubel]] and [[Torsten Wiesel|Wiesel]] (e.g., Hubel, 1963; [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1363130/ Hubel-Wiesel 1959]) classified receptive fields of cells in the visual cortex into [[simple cell]]s, [[complex cell]]s, and [[hypercomplex cell]]s. Simple cell receptive fields are elongated, for example with an excitatory central oval, and an inhibitory surrounding region, or approximately rectangular, with one long side being excitatory and the other being inhibitory. Images for these receptive fields need to have a particular orientation in order to excite the cell. For complex-cell receptive fields, a correctly oriented bar of light might need to move in a particular direction in order to excite the cell. For hypercomplex receptive fields, the bar might also need to be of a particular length. {|class="wikitable" |+ Original Organization of Visual Processing Cells by Hubel and Wiesel ! Cell Type !! Selectivity !! Location |- |Simple||orientation, position||[[Brodmann area]] 17 |- |Complex||orientation, motion, direction||Brodmann area 17 and 18 |- |Hypercomplex||orientation, motion, direction, length||Brodmann areas 18 and 19 |} ===Extrastriate visual areas=== In extrastriate visual areas, cells can have very large receptive fields requiring very complex images to excite the cell. For example, in the [[inferotemporal cortex]], receptive fields cross the midline of visual space and require images such as radial gratings or hands. It is also believed that in the [[fusiform face area]], images of faces excite the cortex more than other images. This property was one of the earliest major results obtained through [[Functional magnetic resonance imaging|fMRI]] ([[Nancy Kanwisher|Kanwisher]], McDermott and Chun, 1997); the finding was confirmed later at the neuronal level ([[Doris Tsao|Tsao]], Freiwald, Tootell and [[Margaret Livingstone|Livingstone]], 2006). In a similar vein, people have looked for other category-specific areas and found evidence for regions representing views of places ([[parahippocampal place area]]) and the body ([[Extrastriate body area]]). However, more recent research has suggested that the fusiform face area is specialised not just for faces, but also for any discrete, within-category discrimination.<ref>{{cite journal|pmid = 23027970|doi=10.1073/pnas.1116333109|volume=109|issue=42|title=High-resolution imaging of expertise reveals reliable object selectivity in the fusiform face area related to perceptual performance|pmc=3479484|journal=Proc Natl Acad Sci U S A|pages=17063β8|last1 = McGugin|first1 = RW|last2 = Gatenby|first2 = JC|last3 = Gore|first3 = JC|last4 = Gauthier|first4 = I|year=2012 |bibcode=2012PNAS..10917063M|doi-access=free}}</ref> ===Computational theory of visual receptive fields=== A theoretical explanation of the computational function of visual receptive fields is given in.<ref name=Lin13BICY>[https://dx.doi.org/10.1007/s00422-013-0569-z T. Lindeberg "A computational theory of visual receptive fields", Biological Cybernetics 107(6): 589-635, 2013]</ref><ref name=Lin21Heliyon>[https://doi.org/10.1016/j.heliyon.2021.e05897 T. Lindeberg "Normative theory of visual receptive fields", Heliyon 7(1):e05897, 2021.]</ref><ref name=Lin23Front>[https://dx.doi.org/10.3389/fncom.2023.1189949 T. Lindeberg "Covariance properties under natural image transformations for the generalized Gaussian derivative model for visual receptive fields", Frontiers in Computational Neuroscience, 17:1189949, 2023.]</ref> It is described how idealised models of receptive fields similar to the biological receptive fields<ref>G. C. DeAngelis, I. Ohzawa and R. D. Freeman "Receptive field dynamics in the central visual pathways". Trends Neurosci. 18(10), 451β457, 1995.</ref><ref>G. C. DeAngelis and A. Anzai "A modern view of the classical receptive field: linear and non-linear spatio-temporal processing by V1 neurons. In: Chalupa, L.M., Werner, J.S. (eds.) The Visual Neurosciences, vol. 1, pp. 704β719. MIT Press, Cambridge, 2004.</ref> found in the retina, the LGN and the primary visual cortex can be derived from structural properties of the environment in combination with internal consistency to guarantee consistent representation of image structures over multiple spatial and temporal scales. It is also described how the receptive fields in the primary visual cortex, which are tuned to different sizes, orientations and directions in the image domain, enable the visual system to handle the influence of natural image transformations and to compute invariant image representations at higher levels in the visual hierarchy. An in-depth theoretical analysis of how the orientation selectivity of simple cells and complex cells in the primary visual cortex relate to inherent properties of visual receptive fields is given in.<ref name=Lin25>[https://doi.org/10.1007/s10827-024-00888-w T. Lindeberg (2025) "Orientation selectivity properties for the affine Gaussian derivative and the affine Gabor models for visual receptive fields", Journal of Computational Neuroscience.]</ref>
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