Receptive Regions Of A Neuron

Delimited medium where some stimuli can evoke neuronal responses

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

Complexity of the receptive field ranges from the unidimensional chemical structure of odorants to the multidimensional spacetime of human visual field, through the bidimensional pare surface, existence a receptive field for touch perception. Receptive fields can positively or negatively alter the membrane potential with or without affecting the rate of activity potentials.^[1]

A sensory space can exist dependent of an animal's location. For a particular sound wave traveling in an appropriate transmission medium, by ways of audio localization, an auditory space would amount to a reference system that continuously shifts as the animal moves (taking into consideration the space inside the ears also). Conversely, receptive fields tin can exist largely independent of the animal's location, as in the instance of place cells. A sensory space can also map into a item region on an animal's body. For example, it could exist a pilus in the cochlea or a slice of skin, retina, or tongue or other part of an brute'southward body. Receptive fields accept been identified for neurons of the auditory system, the somatosensory system, and the visual system.

The term receptive field was first used past Sherrington in 1906 to draw the expanse of pare from which a scratch reflex could be elicited in a canis familiaris.^[2] In 1938, Hartline started to apply the term to unmarried neurons, this time from the frog retina.^[1]

This concept of receptive fields tin be extended further upward the nervous arrangement. If many sensory receptors all form synapses with a single cell farther up, they collectively class the receptive field of that cell. For example, the receptive field of a ganglion cell in the retina of the centre is composed of input from all of the photoreceptors which synapse with it, and a grouping of ganglion cells in turn forms the receptive field for a cell in the brain. This process is chosen convergence.

Receptive fields have been used in modern bogus deep neural networks that piece of work with local operations.

Auditory system [edit]

The auditory system processes the temporal and spectral (i.eastward. frequency) characteristics of audio waves, so the receptive fields of neurons in the auditory arrangement are modeled as spectro-temporal patterns that crusade the firing rate of the neuron to attune with the auditory stimulus. Auditory receptive fields are often modeled as spectro-temporal receptive fields (STRFs), which are the specific pattern in the auditory domain that causes modulation of the firing charge per unit of a neuron. Linear STRFs are created past start calculating a spectrogram of the acoustic stimulus, which determines how the spectral density of the acoustic stimulus changes over time, often using the Short-time Fourier transform (STFT). Firing rate is modeled over time for the neuron, possibly using a peristimulus time histogram if combining over multiple repetitions of the audio-visual stimulus. Then, linear regression is used to predict the firing charge per unit of that neuron as a weighted sum of the spectrogram. The weights learned by the linear model are the STRF, and represent the specific acoustic pattern that causes modulation in the firing rate of the neuron. STRFs can also be understood every bit the transfer office that maps an acoustic stimulus input to a firing rate response output.^[three] A theoretical explanation of the computational part of early auditory receptive fields is given in.^[4]

Somatosensory organisation [edit]

In the somatosensory system, receptive fields are regions of the skin or of internal organs. Some types of mechanoreceptors have big receptive fields, while others take smaller ones.

Big receptive fields allow the jail cell to detect changes over a wider expanse, but lead to a less precise perception. Thus, the fingers, which require the ability to discover fine detail, have many, densely packed (upward to 500 per cubic cm) mechanoreceptors with small-scale receptive fields (around 10 square mm), while the back and legs, for example, have fewer receptors with large receptive fields. Receptors with large receptive fields usually have a "hot spot", an area within the receptive field (usually in the center, directly over the receptor) where stimulation produces the almost intense response.^{[ citation needed ]}

Tactile-sense-related cortical neurons have receptive fields on the skin that tin can exist modified by experience or by injury to sensory nerves resulting in changes in the field'south size and position. In general these neurons take relatively large receptive fields (much larger than those of dorsal root ganglion cells). However, the neurons are able to discriminate fine detail due to patterns of excitation and inhibition relative to the field which leads to spatial resolution.

Visual system [edit]

In the visual system, receptive fields are volumes in visual infinite. They are smallest in the fovea where they can be a few minutes of arc like a dot on this page, to the whole page. For case, the receptive field of a unmarried photoreceptor is a cone-shaped volume comprising all the visual directions in which lite will modify the firing of that jail cell. Its noon is located in the center of the lens and its base of operations essentially at infinity in visual space. Traditionally, visual receptive fields were portrayed in two dimensions (eastward.g., as circles, squares, or rectangles), but these are simply slices, cut along the screen on which the researcher presented the stimulus, of the book of infinite 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 sure interval of altitude from the animal, or from where the eyes are fixating (run across Panum'due south area).

The receptive field is often identified as the region of the retina where the action of low-cal alters the firing of the neuron. In retinal ganglion cells (see below), this expanse of the retina would encompass all the photoreceptors, all the rods and cones from one heart that are connected to this particular ganglion prison cell via bipolar cells, horizontal cells, and amacrine cells. 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 center, the full influence on the neuron's firing is revealed only when both eyes are open.

Hubel and Wiesel ^[5] advanced the theory that receptive fields of cells at i level of the visual system are formed from input past cells at a lower level of the visual arrangement. In this way, small, unproblematic receptive fields could be combined to grade large, complex receptive fields. Later theorists elaborated this elementary, hierarchical organization past allowing cells at one level of the visual system to be influenced past feedback from higher levels.

Receptive fields take been mapped for all levels of the visual arrangement 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 whatsoever one location are contingent on the activities of neurons across the whole arrangement, i.east. are contingent on changes in the whole field, information technology is unclear whether a local description of a particular "receptive field" tin can be considered a full general description, robust to changes in the field every bit a whole. Studies based on perception practice not give the total picture of the understanding of visual phenomena, so the electrophysiological tools must be used, as the retina, after all, is an outgrowth of the encephalon.

In retinal ganglion and V1 cells, the receptive field consists of the center and environs region.

Retinal ganglion cells [edit]

On center and off heart retinal ganglion cells respond oppositely to light in the center and environs of their receptive fields. A potent response means high frequency firing, a weak response is firing at a low frequency, and no response ways no action potential is fired.

A computer emulation of "border detection" using retinal receptive fields. On-center and off-eye stimulation is shown in red and green respectively.

Each ganglion cell or optic nervus fiber bears a receptive field, increasing with intensifying light. In the largest field, the lite has to be more intense at the periphery of the field than at the heart, showing that some synaptic pathways are more preferred than others.

The organisation of ganglion cells' receptive fields, composed of inputs from many rods and cones, provides a way of detecting contrast, and is used for detecting objects' edges.^[six] ^: 188 Each receptive field is arranged into a central disk, the "eye", and a concentric ring, the "environs", each region responding oppositely to low-cal. For example, calorie-free in the center might increase the firing of a item ganglion cell, whereas calorie-free 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 increment in the firing of the ganglion jail cell, stimulation of the environs produces a hyperpolarization and a decrease in the firing of the cell, and stimulation of both the centre and environs produces only a balmy response (due to mutual inhibition of center and surround). An off-middle prison cell is stimulated past activation of the surround and inhibited by stimulation of the middle (see figure).

Photoreceptors that are part of the receptive fields of more than than one ganglion cell are able to excite or inhibit postsynaptic neurons because they release the neurotransmitter glutamate at their synapses, which can act to depolarize or to hyperpolarize a cell, depending on whether there is a metabotropic or ionotropic receptor on that cell.

The eye-surround receptive field organization allows ganglion cells to transmit data non but nearly whether photoreceptor cells are exposed to light, but also about the differences in firing rates of cells in the heart and surround. This allows them to transmit information about contrast. The size of the receptive field governs the spatial frequency of the data: small receptive fields are stimulated past loftier spatial frequencies, fine detail; big receptive fields are stimulated past depression spatial frequencies, coarse particular. Retinal ganglion cell receptive fields convey information about discontinuities in the distribution of light falling on the retina; these frequently specify the edges of objects. In dark accommodation, the peripheral contrary activeness zone becomes inactive, just, since it is a diminishing of inhibition between eye and periphery, the agile field tin can really increase, allowing more area for summation.

Lateral geniculate nucleus [edit]

Further forth 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-environs organization and cells that are either on- or off centre.

Visual cortex [edit]

Receptive fields of cells in the visual cortex are larger and take more-complex stimulus requirements than retinal ganglion cells or lateral geniculate nucleus cells. Hubel and Wiesel (e.k., Hubel, 1963; Hubel-Wiesel 1959) classified receptive fields of cells in the visual cortex into simple cells, complex cells, and hypercomplex cells. Elementary 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 existence inhibitory. Images for these receptive fields need to have a item orientation in order to excite the cell. For complex-cell receptive fields, a correctly oriented bar of light might demand to move in a particular management in order to excite the cell. For hypercomplex receptive fields, the bar might likewise demand to be of a detail length.

Original Organization of Visual Processing Cells by Hubel and Wiesel
Cell Type	Selectivity	Location
Simple	orientation, position	Brodmann area 17
Complex	orientation, move, management	Brodmann area 17 and 18
Hypercomplex	orientation, motion, direction, length	Brodmann areas xviii and 19

[edit]

In extrastriate visual areas, cells can have very large receptive fields requiring very circuitous images to excite the prison cell. For example, in the inferotemporal cortex, receptive fields cantankerous the midline of visual space and require images such every bit radial gratings or easily. Information technology 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 fMRI (Kanwisher, McDermott and Chun, 1997); the finding was confirmed later at the neuronal level (Tsao, Freiwald, Tootell and Livingstone, 2006). In a similar vein, people accept looked for other category-specific areas and found bear witness for regions representing views of places (parahippocampal place area) and the torso (Extrastriate torso area). However, more than recent research has suggested that the fusiform face area is specialised not just for faces, but as well for any detached, inside-category discrimination.^[7]

Computational theory of visual receptive fields [edit]

A theoretical explanation of the computational role of visual receptive fields is given in.^[8] ^[9] Information technology is described how idealised models of receptive fields like to the biological receptive fields^[ten] ^[11] constitute in the retina, the LGN and the chief visual cortex tin be derived from structural properties of the environs in combination with internal consistency to guarantee consistent representation of image structures over multiple spatial and temporal scales. It is too described how the receptive fields in the master visual cortex, which are tuned to different sizes, orientations and directions in the image domain, enable the visual arrangement to handle the influence of natural image transformations and to compute invariant epitome representations at higher levels in the visual hierarchy.

In the context of neural networks [edit]

Neurons of a convolutional layer (blue), connected to their receptive field (ruby-red)

CNN layers arranged in 3 dimensions

The term receptive field is also used in the context of artificial neural networks, most often in relation to convolutional neural networks (CNNs). Then, in a neural network context, the receptive field is defined equally the size of the region in the input that produces the feature. Basically, it is a measure of association of an output feature (of any layer) to the input region (patch). It is important to notation that the idea of receptive fields applies to local operations (i.e. convolution, pooling). As an instance, in motion-based tasks, like video prediction and optical catamenia estimation, large motions need to be captured (displacements of pixels in a second grid), so an adequate receptive field is required. Specifically, the receptive field should be sufficient if it is larger than the largest flow magnitude of the dataset. There are a lot of ways that one tin increase the receptive field on a CNN.

When used in this sense, the term adopts a meaning reminiscent of receptive fields in actual biological nervous systems. CNNs accept a distinct architecture, designed to mimic the way in which existent brute brains are understood to function; instead of having every neuron in each layer connect to all neurons in the side by side layer (Multilayer perceptron), the neurons are bundled in a iii-dimensional structure in such a way as to take into account the spatial relationships betwixt different neurons with respect to the original information. Since CNNs are used primarily in the field of computer vision, the data that the neurons correspond is typically an epitome; each input neuron represents one pixel from the original paradigm. The first layer of neurons is composed of all the input neurons; neurons in the adjacent layer will receive connections from some of the input neurons (pixels), but non all, every bit would exist the example in a MLP and in other traditional neural networks. Hence, instead of having each neuron receive connections from all neurons in the previous layer, CNNs use a receptive field-like layout in which each neuron receives connections only from a subset of neurons in the previous (lower) layer. The receptive field of a neuron in one of the lower layers encompasses simply a small area of the paradigm, while the receptive field of a neuron in subsequent (college) layers involves a combination of receptive fields from several (but not all) neurons in the layer earlier (i. e. a neuron in a college layer "looks" at a larger portion of the image than does a neuron in a lower layer). In this way, each successive layer is capable of learning increasingly abstruse features of the original image. The use of receptive fields in this mode is thought to give CNNs an advantage in recognizing visual patterns when compared to other types of neural networks.

Meet also [edit]

Visual system
Reflexogenic zone
Spatiotemporal receptive field
Spectro-temporal receptive field
Computer vision
Border detection
Convolutional neural network

References [edit]

^ ^a ^b ^c Alonso, J.-Thousand.; Chen, Y. (2008). "Receptive field". Scholarpedia. 4 (1): 5393. doi:10.4249/scholarpedia.5393. Archived from the original on 2021-01-21.
^ Sherrington, C. South. (1906). "Observations on the scratch-reflex in the spinal canis familiaris". Journal of Physiology. 34 (ane–2): one–fifty. doi:10.1113/jphysiol.1906.sp001139. PMC1465804. PMID 16992835.
^ Theunissen, F.E.; David, Southward.V.; Singh, North.C.; Hsu, A.; Vinje, W.Due east.; Gallant, J.50. (2001). "Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli". Network: Ciphering in Neural Systems. 12 (3): 289–316. doi:10.1080/net.12.three.289.316. PMID 11563531. S2CID 199667772.
^ Lindeberg, T; Friberg, A. (2015). "Idealized computational models for auditory receptive fields". PLOS Ane. x (3): e0119032. arXiv:1404.2037. Bibcode:2015PLoSO..1019032L. doi:10.1371/journal.pone.0119032. PMC4379182. PMID 25822973.
^ e.one thousand., Hubel, 1963; Hubel-Wiesel, 1962
^ Higgs, Suzanne (2014-12-19). Biological psychology. Cooper, Alison (Senior lecturer in neurobiology),, Lee, Jonathan (Neuroscientist),, Harris, Mike (Mike Grand.). Los Angeles. ISBN9780857022622. OCLC 898753111.
^ McGugin, RW; Gatenby, JC; Gore, JC; Gauthier, I (2012). "High-resolution imaging of expertise reveals reliable object selectivity in the fusiform face up area related to perceptual performance". Proc Natl Acad Sci U S A. 109 (42): 17063–8. Bibcode:2012PNAS..10917063M. doi:10.1073/pnas.1116333109. PMC3479484. PMID 23027970.
^ T. Lindeberg "A computational theory of visual receptive fields", Biological Cybernetics 107(6): 589-635, 2013
^ T. Lindeberg "Normative theory of visual receptive fields", Heliyon 7(ane):e05897, 2021.
^ Thousand. C. DeAngelis, I. Ohzawa and R. D. Freeman "Receptive field dynamics in the central visual pathways". Trends Neurosci. 18(10), 451–457, 1995.
^ Chiliad. 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.South. (eds.) The Visual Neurosciences, vol. 1, pp. 704–719. MIT Press, Cambridge, 2004.

Hubel, D. H. (1963). "The visual cortex of the encephalon". Scientific American. 209 (5): 54–62. Bibcode:1963SciAm.209e..54H. doi:10.1038/scientificamerican1163-54. PMID 14075682.
Kandel Due east.R., Schwartz, J.H., Jessell, T.M. (2000). Principles of Neural Science, 4th ed., pp. 515–520. McGraw-Hill, New York.

External links [edit]

Receptive Fields Tutorial