Simple cell

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Simple cell
Gabor filter.png
Gabor filter-type receptive field typical for a simple cell. Blue regions indicate inhibition, red facilitation
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Part of primary visual cortex
System Visual system
Anatomical terminology

A simple cell in the primary visual cortex is a cell that responds primarily to oriented edges and gratings (bars of particular orientations). These cells were discovered by Torsten Wiesel and David Hubel in the late 1950s. [1]

Contents

Such cells are tuned to different frequencies and orientations, even with different phase relationships, possibly for extracting disparity (depth) information and to attribute depth to detected lines and edges. [2] This may result in a 3D 'wire-frame' representation as used in computer graphics. The fact that input from the left and right eyes is very close in the so-called cortical hypercolumns is an indication that depth processing occurs at a very early stage, aiding recognition of 3D objects.

Later, many other cells with specific functions have been discovered: (a) end-stopped cells which are thought to detect singularities like line and edge crossings, vertices and line endings; (b) bar and grating cells. The latter are not linear operators because a bar cell does not respond when seeing a bar which is part of a periodic grating, and a grating cell does not respond when seeing an isolated bar.

Using the mathematical Gabor model with sine and cosine components (phases), complex cells are then modeled by computing the modulus of complex Gabor responses. Both simple and complex cells are linear operators and are seen as filters because they respond selectively to a large number of patterns.

However, it has been claimed that the Gabor model does not conform to the anatomical structure of the visual system as it short-cuts the LGN and uses the 2D image as it is projected on the retina. Azzopardi and Petkov [3] have proposed a computational model of a simple cell, which combines the responses of model LGN cells with center-surround receptive fields (RFs). They call it Combination of RFs (CORF) model. Besides orientation selectivity, it exhibits cross orientation suppression, contrast invariant orientation tuning and response saturation. These properties are observed in real simple cells but are not possessed by the Gabor model. Using simulated reverse correlation they also demonstrate that the RF map of the CORF model can be divided into elongated excitatory and inhibitory regions typical of simple cells.

Lindeberg [4] [5] has derived axiomatically determined models of simple cells in terms of directional derivatives of affine Gaussian kernels over the spatial domain in combination with temporal derivatives of either non-causal or time-causal scale-space kernels over the temporal domain and shown that this theory both leads to predictions about receptive fields with good qualitative agreement with the biological receptive field measurements performed by DeAngelis et al. [6] [7] and guarantees good theoretical properties of the mathematical receptive field model, including covariance and invariance properties under natural image transformations. [8] [9]

History

These cells were discovered by Torsten Wiesel and David Hubel in the late 1950s. [10]

Hubel and Wiesel named these cells "simple," as opposed to "complex cell", because they shared the properties: [11]

  1. They have distinct excitatory and inhibitory regions.
  2. These regions follow the summation property.
  3. These regions have mutual antagonism - excitatory and inhibitory regions balance themselves out in diffuse lighting.
  4. It is possible to predict responses of moving stimuli given the map of excitatory and inhibitory regions.

Some other researchers such as Peter Bishop and Peter Schiller used different definitions for simple and complex cells. [12]

Related Research Articles

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Computational neuroscience is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.

<span class="mw-page-title-main">Visual system</span> Body parts responsible for vision

The visual system is the physiological basis of visual perception. The system detects, transduces and interprets information concerning light within the visible range to construct an image and build a mental model of the surrounding environment. The visual system is associated with the eye and functionally divided into the optical system and the neural system.

<span class="mw-page-title-main">Lateral geniculate nucleus</span> Component of the visual system in the brains thalamus

In neuroanatomy, the lateral geniculate nucleus is a structure in the thalamus and a key component of the mammalian visual pathway. It is a small, ovoid, ventral projection of the thalamus where the thalamus connects with the optic nerve. There are two LGNs, one on the left and another on the right side of the thalamus. In humans, both LGNs have six layers of neurons alternating with optic fibers.

<span class="mw-page-title-main">David H. Hubel</span> Canadian neurophysiologist

David Hunter Hubel was an American Canadian neurophysiologist noted for his studies of the structure and function of the visual cortex. He was co-recipient with Torsten Wiesel of the 1981 Nobel Prize in Physiology or Medicine, for their discoveries concerning information processing in the visual system. For much of his career, Hubel worked as the Professor of Neurobiology at Johns Hopkins University and Harvard Medical School. In 1978, Hubel and Wiesel were awarded the Louisa Gross Horwitz Prize from Columbia University. In 1983, Hubel received the Golden Plate Award of the American Academy of Achievement.

The receptive field, or sensory space, is a delimited medium where some physiological stimuli can evoke a sensory neuronal response in specific organisms.

In neuroscience, neuronal tuning refers to the hypothesized property of brain cells by which they selectively represent a particular type of sensory, association, motor, or cognitive information. Some neuronal responses have been hypothesized to be optimally tuned to specific patterns through experience. Neuronal tuning can be strong and sharp, as observed in primary visual cortex, or weak and broad, as observed in neural ensembles. Single neurons are hypothesized to be simultaneously tuned to several modalities, such as visual, auditory, and olfactory. Neurons hypothesized to be tuned to different signals are often hypothesized to integrate information from the different sources. In computational models called neural networks, such integration is the major principle of operation. The best examples of neuronal tuning can be seen in the visual, auditory, olfactory, somatosensory, and memory systems, although due to the small number of stimuli tested the generality of neuronal tuning claims is still an open question.

The spectro-temporal receptive field or spatio-temporal receptive field (STRF) of a neuron represents which types of stimuli excite or inhibit that neuron. "Spectro-temporal" refers most commonly to audition, where the neuron's response depends on frequency versus time, while "spatio-temporal" refers to vision, where the neuron's response depends on spatial location versus time. Thus they are not exactly the same concept, but both are referred to as STRF and serve a similar role in the analysis of neural responses.

<span class="mw-page-title-main">Lateral inhibition</span> Capacity of an excited neuron to reduce activity of its neighbors

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The neocognitron is a hierarchical, multilayered artificial neural network proposed by Kunihiko Fukushima in 1979. It has been used for Japanese handwritten character recognition and other pattern recognition tasks, and served as the inspiration for convolutional neural networks.

Complex cells can be found in the primary visual cortex (V1), the secondary visual cortex (V2), and Brodmann area 19 (V3).

<span class="mw-page-title-main">Hypercomplex cell</span>

A hypercomplex cell is a type of visual processing neuron in the mammalian cerebral cortex. Initially discovered by David Hubel and Torsten Wiesel in 1965, hypercomplex cells are defined by the property of end-stopping, which is a decrease in firing strength with increasingly larger stimuli. The sensitivity to stimulus length is accompanied by selectivity for the specific orientation, motion, and direction of stimuli. For example, a hypercomplex cell may only respond to a line at 45˚ that travels upward. Elongating the line would result in a proportionately weaker response. Ultimately, hypercomplex cells can provide a means for the brain to visually perceive corners and curves in the environment by identifying the ends of a given stimulus.

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.

Robert Shapley is an American neurophysiologist, the Natalie Clews Spencer Professor of the Sciences at New York University, a professor in the Center for Neural Science and an associate member of the Courant Institute of Mathematical Sciences.

<span class="mw-page-title-main">Orientation column</span>

Orientation columns are organized regions of neurons that are excited by visual line stimuli of varying angles. These columns are located in the primary visual cortex (V1) and span multiple cortical layers. The geometry of the orientation columns are arranged in slabs that are perpendicular to the surface of the primary visual cortex.

<span class="mw-page-title-main">Visual tilt effects</span>

Due to the effect of a spatial context or temporal context, the perceived orientation of a test line or grating pattern can appear tilted away from its physical orientation. The tilt illusion (TI) is the phenomenon that the perceived orientation of a test line or grating is altered by the presence of surrounding lines or grating with a different orientation. And the tilt aftereffect (TAE) is the phenomenon that the perceived orientation is changed after prolonged inspection of another oriented line or grating.

Binocular neurons are neurons in the visual system that assist in the creation of stereopsis from binocular disparity. They have been found in the primary visual cortex where the initial stage of binocular convergence begins. Binocular neurons receive inputs from both the right and left eyes and integrate the signals together to create a perception of depth.

Surround suppression is where the relative firing rate of a neuron may under certain conditions decrease when a particular stimulus is enlarged. It has been observed in electrophysiology studies of the brain and has been noted in many sensory neurons, most notably in the early visual system. Surround suppression is defined as a reduction in the activity of a neuron in response to a stimulus outside its classical receptive field.

<span class="mw-page-title-main">Russell L. De Valois</span>

Russell L. De Valois was an American scientist recognized for his pioneering research on spatial and color vision.

Orientation selectivity is expressed by cells within the visual cortex, when such cells increase impulse or signal activity for specific oriented degree of shape presented within the visual field. Orientation selectivity can also be expressed by simple cells if the orientation of a stimulus is orthogonal to the preferred degree of orientation, which results in the inhibition of impulse activity.

References

  1. D. H. Hubel and T. N. Wiesel Receptive Fields of Single Neurones in the Cat's Striate Cortex J. Physiol. pp. 574-591 (148) 1959
  2. Freeman, R. D.; DeAngelis, G. C.; Ohzawa, I. (1990-08-31). "Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors". Science. 249 (4972): 1037–1041. Bibcode:1990Sci...249.1037O. CiteSeerX   10.1.1.473.8284 . doi:10.1126/science.2396096. ISSN   1095-9203. PMID   2396096.
  3. G. Azzopardi and N. Petkov A CORF computational model that relies on LGN input outperforms the Gabor function model Biological Cybernetics, vol. 106(3), pp. 177-189, DOI: 10.1007/s00422-012-0486-6, 2012
  4. Lindeberg, Tony (2013). "A computational theory of visual receptive fields". Biological Cybernetics. 107 (6): 589–635. doi:10.1007/s00422-013-0569-z. PMC   3840297 . PMID   24197240.
  5. Lindeberg, Tony (2021). "Normative theory of visual receptive fields". Heliyon. 7 (1): e05897. doi: 10.1016/j.heliyon.2021.e05897 . PMC   7820928 . PMID   33521348.
  6. G. C. DeAngelis, I. Ohzawa and R. D. Freeman "Receptive field dynamics in the central visual pathways". Trends Neurosci. 18(10), 451–457, 1995.
  7. 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. 704–719. MIT Press, Cambridge, 2004.
  8. Lindeberg, Tony (2013). "Invariance of visual operations at the level of receptive fields". PLOS ONE. 8 (7): e66990. doi: 10.1371/journal.pone.0066990 . PMC   3716821 . PMID   23894283.
  9. Lindeberg, Tony (2023). "Covariance properties under natural image transformations for the generalised Gaussian derivative model for visual receptive fields". Frontiers in Computational Neuroscience. 17. doi: 10.3389/fncom.2023.1189949 . PMC   10311448 . PMID   37398936.
  10. D. H. Hubel and T. N. Wiesel Receptive Fields of Single Neurones in the Cat's Striate Cortex J. Physiol. pp. 574-591 (148) 1959
  11. D. H. Hubel and T. N. Wiesel Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex J. Physiol. 160 pp. 106-154 1962
  12. Brain and Visual Perception: The Story of a 25-Year Collaboration D. H. Hubel and T. N. Wiesel Oxford 2005

See also