Visual cortex: Difference between revisions

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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>{{citeCite thesisjournal |last=Lamme vauthors|first=Victor A.F. |last2=Roelfsema Barghout|first2=Pieter LR. |date=2000-11 |title=Vision:The Howdistinct Globalmodes Perceptualof Contextvision Changesoffered Localby Contrastfeedforward Processingand |recurrent degreeprocessing |url= Phhttps://linkinghub.Delsevier.com/retrieve/pii/S016622360001657X |datejournal=2003Trends in Neurosciences |publishervolume=Scholar's23 Press|issue=11 |isbnpages=978-3-639-70962-9571–579 |urldoi= https://www10.morebooks.de1016/store/gb/book/vision/isbn/978s0166-32236(00)01657-639x |issn=0166-70962-9 2236}} 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 = 2–3 | pages = 141–54 | date = 2003 | pmid = 14766139 | doi = 10.1016/j.jphysparis.2003.09.001 }}</ref><ref name="Bullier_2001">{{cite book | vauthors = Bullier J, Hupé JM, James AC, Girard P | title = The role of feedback connections in shaping the responses of visual cortical neurons | chapter = Chapter 13 the role of feedback connections in shaping the responses of visual cortical neurons | series = Progress in Brain Research | volume = 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.<ref name="Murray_2004">{{cite journal | vauthors = Murray SO, Schrater P, Kersten D | title = Perceptual grouping and the interactions between visual cortical areas | journal = Neural Networks | volume = 17 | issue = 5–6 | pages = 695–705 | date = 2004 | pmid = 15288893 | doi = 10.1016/j.neunet.2004.03.010 }}</ref><ref name="Huang_2007">{{cite journal | vauthors = Huang JY, Wang C, Dreher B | title = The effects of reversible inactivation of postero-temporal visual cortex on neuronal activities in cat's area 17 | journal = Brain Research | volume = 1138 | issue = | pages = 111–28 | date = March 2007 | pmid = 17276420 | doi = 10.1016/j.brainres.2006.12.081 }}</ref><ref name="Williams_2008">{{cite journal | vauthors = Williams MA, Baker CI, Op de Beeck HP, Shim WM, Dang S, Triantafyllou C, Kanwisher N | title = Feedback of visual object information to foveal retinotopic cortex | journal = Nature Neuroscience | volume = 11 | issue = 12 | pages = 1439–45 | date = December 2008 | pmid = 18978780 | pmc = 2789292 | doi = 10.1038/nn.2218 }}</ref>

The visual information relayed toby V1 is not coded in terms of spatial (or optical) imagery{{citation needed|date=July 2020}} but rather are bettersometimes 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 |chapter-url=https://direct.mit.edu/books/book/5395/chapter/3948206/A-Modern-View-ofthe-Classical-Receptive-Field |chapter=A Modern View of the 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 |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 = PLOS ONE | 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/>