Visual cortex: Difference between revisions

Revision as of 05:27, 6 July 2024

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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 | vauthors = Fişek M, Herrmann D, Egea-Weiss A, Cloves M, Bauer L, Lee TY, Russell LE, Häusser M | title = Cortico-cortical feedback engages active dendrites in visual cortex | journal = Nature | volume = 617 | issue = 7962 | pages = 769–776 | date = May 2023 | pmid = 37138089 | pmc = 10244179 | doi = 10.1038/s41586-023-06007-6 | bibcode = 2023Natur.617..769F }}</ref>

The visual cortex receives its blood supply primarily from the [[calcarine artery|calcarine branch]] of the [[posterior cerebral artery]].

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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 | vauthors = Wu F, Lu Q, Kong Y, Zhang Z | title = A Comprehensive Overview of the Role of Visual Cortex Malfunction in Depressive Disorders: Opportunities and Challenges | journal = Neuroscience Bulletin | volume = 39 | issue = 9 | pages = 1426–1438 | date = September 2023 | pmid = 36995569 | pmc = 10062279 | doi = 10.1007/s12264-023-01052-7 | pmc-embargo-date = September 1, 2024 }}</ref>

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 | vauthors = Barghout L |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 tuning properties of V1 neurons (what the neurons respond to) differ greatly over time. Early in time (40 ms and further) individual V1 neurons have strong tuning to a small set of stimuli. That is, the neuronal responses can discriminate small changes in visual [[Orientation (mental)|orientations]], [[spatial frequencies]] and [[color]]s (as in the optical system of a [[camera obscura]], but projected onto [[retina]]l cells of the eye, which are clustered in density and fineness).<ref name= kepler1604 /> Each V1 neuron propagates a signal from a retinal cell, in continuation. Furthermore, individual V1 neurons in humans and other animals with [[binocular vision]] have ocular dominance, namely tuning to one of the two eyes. In V1, and primary sensory cortex in general, neurons with similar tuning properties tend to cluster together as [[cortical column]]s. [[David Hubel]] and [[Torsten Wiesel]] proposed the classic ice-cube organization model of cortical columns for two tuning properties: [[ocular dominance columns|ocular dominance]] and orientation. However, this model cannot accommodate the color, spatial frequency and many other features to which neurons are tuned {{Citation needed|date=November 2011}}. The exact organization of all these cortical columns within V1 remains a hot topic of current research. The mathematical modeling of this function has been compared to [[Gabor transform]]s.{{Citation needed|date=May 2023}}

Later in time (after 100 ms), neurons in V1 are also sensitive to the more global organisation of the scene (Lamme & Roelfsema, 2000).<ref>{{cite thesis | vauthors = Barghout L |title=Vision: How Global Perceptual Context Changes Local Contrast Processing | degree = Ph.D. |date=2003 |publisher=Scholar's Press |isbn=978-3-639-70962-9 |url= https://www.morebooks.de/store/gb/book/vision/isbn/978-3-639-70962-9 }} Updated to include computer vision techniques</ref> These response properties probably stem from recurrent [[feedback]] processing (the influence of higher-tier cortical areas on lower-tier cortical areas) and lateral connections from [[Pyramidal cell|pyramidal neurons]].<ref name="Hupé_1998">{{cite journal | vauthors = Hupé JM, James AC, Payne BR, Lomber SG, Girard P, Bullier J | title = Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons | journal = Nature | volume = 394 | issue = 6695 | pages = 784–7 | date = August 1998 | pmid = 9723617 | doi = 10.1038/29537 | bibcode = 1998Natur.394..784H }}</ref> While feedforward connections are mainly driving, feedback connections are mostly modulatory in their effects.<ref name="Angelucci_2003">{{cite journal | vauthors = Angelucci A, Bullier J | title = Reaching beyond the classical receptive field of V1 neurons: horizontal or feedback axons? | journal = Journal of Physiology, Paris | volume = 97 | issue = 2-32–3 | pages = 141–54 | date = 2003 | pmid = 14766139 | doi = 10.1016/j.jphysparis.2003.09.001 }}</ref><ref name="Bullier_2001">{{cite journalbook | vauthors = Bullier J, Hupé JM, James AC, Girard P | title = The role of feedback connections in shaping the responses of visual cortical neurons | journalchapter = ProgressChapter 13 the role of feedback connections in Brainshaping Researchthe responses of visual cortical neurons | volumeseries = 134Progress in Brain Research | issuevolume = 134 | pages = 193–204 | date = 2001 | pmid = 11702544 | doi = 10.1016/s0079-6123(01)34014-1 | isbn = 978-0-444-50586-6 }}</ref> Evidence shows that feedback originating in higher-level areas such as V4, IT, or MT, with bigger and more complex receptive fields, can modify and shape V1 responses, accounting for contextual or extra-classical receptive field effects (Guo et al., 2007; Huang et al., 2007; Sillito et al., 2006).

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 | vauthors = Kesserwani H | title = The Biophysics of Visual Edge Detection: A Review of Basic Principles | journal = Cureus | volume = 12 | issue = 10 | pages = e11218 | date = October 2020 | pmid = 33269147 | pmc = 7706146 | doi = 10.7759/cureus.11218 | doi-access = free }}</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 | title = A computational theory of visual receptive fields | journal = Biological Cybernetics | volume = 107 | issue = 6 | pages = 589–635 | date = December 2013 | pmid = 24197240 | pmc = 3840297 | doi = 10.1007/s00422-013-0569-z }}</ref><ref name=Lin21Heliyon>{{cite journal | vauthors = Lindeberg T | title = Normative theory of visual receptive fields | journal = Heliyon | volume = 7 | issue = 1 | pages = e05897 | date = January 2021 | pmid = 33521348 | pmc = 7820928 | doi = 10.1016/j.heliyon.2021.e05897 | doi-access = free | bibcode = 2021Heliy...705897L }}</ref><ref name=Lin23Front>{{cite journal | vauthors = Lindeberg T | title = Covariance properties under natural image transformations for the generalised Gaussian derivative model for visual receptive fields | journal = Frontiers in Computational Neuroscience | volume = 17 | pages = 1189949 | date = 2023 | pmid = 37398936 | pmc = 10311448 | doi = 10.3389/fncom.2023.1189949 | 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 | title = Receptive-field dynamics in the central visual pathways | journal = Trends in Neurosciences | volume = 18 | issue = 10 | pages = 451–458 | date = October 1995 | pmid = 8545912 | doi = 10.1016/0166-2236(95)94496-r | s2cid = 12827601 }}</ref><ref>{{Cite book | vauthors = DeAngelis GC, Anzai A |chapter-url=https://direct.mit.edu/books/book/5395/chapter/3948206/A-Modern-View-ofthe-Classical-Receptive-Field |titlechapter=A Modern View ofthe Classical Receptive Field: Linear and Nonlinear Spatiotemporal Processing by V1 Neurons |vauthors=DeAngelis GC, Anzai A |title=The Visual Neurosciences, 2-vol. Set |date=2003-11-21 |publisher=The MIT Press |isbn=978-0-262-27012-0 |veditors=Chalupa LM, Werner JS |volume=1 |___location=Cambridge |pages=704–719 |language=en |chapter=A modern view of the classical receptive field: linear and non-linear spatio-temporal processing by V1 neurons |doi=10.7551/mitpress/7131.003.0052 }}</ref> can be derived as a consequence of structural properties of the environment in combination with internal consistency requirements to guarantee consistent image representations over multiple spatial and temporal scales. It is also described how the characteristic receptive field shapes, tuned to different scales, orientations and directions in image space, allow the visual system to compute invariant responses under natural image transformations at higher levels in the visual hierarchy.<ref name=Lin13PONE>{{cite journal | vauthors = Lindeberg T | title = Invariance of visual operations at the level of receptive fields | journal = PloSPLOS OneONE | volume = 8 | issue = 7 | pages = e66990 | year = 2013 | pmid = 23894283 | pmc = 3716821 | doi = 10.1371/journal.pone.0066990 | doi-access = free | arxiv = 1210.0754 | bibcode = 2013PLoSO...866990L }}</ref><ref name=Lin21Heliyon/><ref name=Lin23Front/>

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