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[[Neuron]]s in the visual cortex fire [[action potential]]s when visual stimuli appear within their [[receptive field]]. By definition, the receptive field is the region within the entire visual field that elicits an action potential. But, for any given neuron, it may respond best to a subset of stimuli within its receptive field. This property is called ''[[neuronal tuning]]''. In the earlier visual areas, neurons have simpler tuning. For example, a neuron in V1 may fire to any vertical stimulus in its receptive field. In the higher visual areas, neurons have complex tuning. For example, in the inferior temporal cortex (IT), a neuron may fire only when a certain face appears in its receptive field.
Furthermore, the arrangement of receptive fields in V1 is retinotopic, meaning neighboring cells in V1 have receptive fields that correspond to adjacent portions of the visual field. This spatial organization allows for a systematic representation of the visual world within V1. Additionally, recent studies have delved into the role of contextual modulation in V1, where the perception of a stimulus is influenced not only by the stimulus itself but also by the surrounding context, highlighting the intricate processing capabilities of V1 in shaping our visual experiences.<ref>{{Cite journal |last=Fişek
The visual cortex receives its blood supply primarily from the [[calcarine artery|calcarine branch]] of the [[posterior cerebral artery]].
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Research on V1 has also revealed the presence of orientation-selective cells, which respond preferentially to stimuli with a specific orientation, contributing to the perception of edges and contours. The discovery of these orientation-selective cells has been fundamental in shaping our understanding of how V1 processes visual information.
Furthermore, V1 exhibits plasticity, allowing it to undergo functional and structural changes in response to sensory experience. Studies have demonstrated that sensory deprivation or exposure to enriched environments can lead to alterations in the organization and responsiveness of V1 neurons, highlighting the dynamic nature of this critical visual processing hub.<ref>{{Cite journal |last=Coen
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The correspondence between specific locations in V1 and the subjective visual field is exceptionally precise, even extending to map the blind spots of the retina. Evolutionarily, this correspondence is a fundamental feature found in most animals possessing a V1. In humans and other species with a fovea (cones in the retina), a substantial portion of V1 is mapped to the small central portion of the visual field—a phenomenon termed cortical magnification. This magnification reflects an increased representation and processing capacity devoted to the central visual field, essential for detailed visual acuity and high-resolution processing.
Notably, neurons in V1 have the smallest receptive field size, signifying the highest resolution, among visual cortex microscopic regions. This specialization equips V1 with the ability to capture fine details and nuances in the visual input, emphasizing its pivotal role as a critical hub in early visual processing and contributing significantly to our intricate and nuanced visual perception.<ref>{{Cite journal |last=Wu
In addition to its role in spatial processing, the retinotopic map in V1 is intricately connected with other visual areas, forming a network that contributes to the integration of various visual features and the construction of a coherent visual percept. This dynamic mapping mechanism is fundamental to our ability to navigate and interpret the visual world effectively.<ref name= kepler1604 >Johannes Kepler (1604) Paralipomena to Witelo whereby The Optical Part of Astronomy is Treated (Ad Vitellionem Paralipomena, quibus astronomiae pars optica traditvr, 1604), as cited by A.Mark Smith (2015) From Sight to Light. Kepler modeled the eye as a water-filled glass sphere, and discovered that each point of the scene taken in by the eye projects onto a point on the back of the eye (the retina).</ref> The correspondence between a given ___location in V1 and in the subjective visual field is very precise: even the [[Blind spot (vision)|blind spots]] of the retina are mapped into V1. In terms of evolution, this correspondence is very basic and found in most animals that possess a V1. In humans and other animals with a [[Fovea centralis|fovea]] ([[Cone cell|cones]] in the retina), a large portion of V1 is mapped to the small, central portion of visual field, a phenomenon known as [[cortical magnification]].<ref>{{cite thesis |last1=Barghout |first1=Lauren |title=On the Differences Between Peripheral and Foveal Pattern Masking |date=1999 |type=Masters |publisher=University of California, Berkeley|___location=Berkeley, California}}</ref> Perhaps for the purpose of accurate spatial encoding, neurons in V1 have the smallest [[receptive field]] size (that is, the highest resolution) of any visual cortex microscopic regions.
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The visual information relayed to V1 is not coded in terms of spatial (or optical) imagery{{citation needed|date=July 2020}} but rather are better described as [[edge detection]].<ref>{{cite journal |last1=Kesserwani |first1=Hassan |title=The Biophysics of Visual Edge Detection: A Review of Basic Principles |journal=Cureus |date=28 October 2020 |volume=12 |issue=10 |pages=e11218 |doi=10.7759/cureus.11218 |doi-access=free |pmid=33269147 |pmc=7706146}}</ref> As an example, for an image comprising half side black and half side white, the dividing line between black and white has strongest local contrast (that is, edge detection) and is encoded, while few neurons code the brightness information (black or white per se). As information is further relayed to subsequent visual areas, it is coded as increasingly non-local frequency/phase signals. Note that, at these early stages of cortical visual processing, spatial ___location of visual information is well preserved amid the local contrast encoding (edge detection).
A theoretical explanation of the computational function of the simple cells in the primary visual cortex has been presented in.<ref name=Lin13BICY>{{cite journal |vauthors=Lindeberg T |year=2013| title=A computational theory of visual receptive fields |doi=10.1007/s00422-013-0569-z |pmid=24197240 |pmc=3840297 |journal=Biological Cybernetics |volume=107 |issue=6| pages=589–635}}</ref><ref name=Lin21Heliyon>{{cite journal |vauthors=Lindeberg T |year=2021| title=Normative theory of visual receptive fields |doi=10.1016/j.heliyon.2021.e05897 |journal=Heliyon |volume=7 |issue=1| pages=e05897:1–20 |pmid=33521348 |pmc=7820928 |bibcode=2021Heliy...705897L |doi-access=free}}</ref><ref name=Lin23Front>{{cite journal |doi=10.3389/fncom.2023.1189949 |vauthors=Lindeberg T |title=Covariance properties under natural image transformations for the generalized Gaussian derivative model for visual receptive fields |journal=Frontiers in Computational Neuroscience |volume=17 |at=1189949 |date=2023 |pmid=37398936 |pmc=10311448 |doi-access=free }}</ref> It is described how receptive field shapes similar to those found by the biological receptive field measurements performed by DeAngelis et al.<ref>{{cite journal |vauthors=DeAngelis GC, Ohzawa I, Freeman RD |year=1995 |title=Receptive field dynamics in the central visual pathways |journal=Trends in Neurosciences |volume=18 |issue=10| pages=451–457 |doi=10.1016/0166-2236(95)94496-r |pmid=8545912| s2cid=12827601}}</ref><ref>{{
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