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• W1978220087 abstract "Motion vision provides essential cues for navigation and course control as well as for mate, prey, or predator detection. Consequently, neurons responding to visual motion in a direction-selective way are found in almost all species that see. However, directional information is not explicitly encoded at the level of a single photoreceptor. Rather, it has to be computed from the spatio-temporal excitation level of at least two photoreceptors. How this computation is done and how this computation is implemented in terms of neural circuitry and membrane biophysics have remained the focus of intense research over many decades. Here, we review recent progress made in this area with an emphasis on insects and the vertebrate retina. Motion vision provides essential cues for navigation and course control as well as for mate, prey, or predator detection. Consequently, neurons responding to visual motion in a direction-selective way are found in almost all species that see. However, directional information is not explicitly encoded at the level of a single photoreceptor. Rather, it has to be computed from the spatio-temporal excitation level of at least two photoreceptors. How this computation is done and how this computation is implemented in terms of neural circuitry and membrane biophysics have remained the focus of intense research over many decades. Here, we review recent progress made in this area with an emphasis on insects and the vertebrate retina. Motion vision serves many different tasks; when moving through the environment, the images of the environment as projected onto the photoreceptor layer are constantly in motion. Since the particular distribution of motion vectors on the retina, called optic flow, depends on the specific movement of the animal, whether it is moving forward or making a turn, the optic flow represents a rich source of information that is widely used for navigation and visual course control. Motion cues also occur when the observing animal is standing still but another animal is moving. Obviously, detecting such a potential mate, prey, or predator and knowing which direction it is moving can be of utmost importance for the survival of the observer. Thus, it is not surprising that neurons responding to visual motion cues in a direction-selective (DS) way are found in different parts of the nervous system across the animal kingdom. However, despite the high behavioral relevance of motion vision, the direction of motion is not encoded explicitly by the signals of individual photoreceptors: When moving a bar from left to right and back again, the output signal of a photoreceptor will be the same both times, no matter in which direction the bar has been moving. However, a few synapses downstream into the nervous system, cells are found that respond differently to the two directions. In between, some computation is happening, turning the direction unselective response of the photoreceptor into a DS response of the interneuron. This problem has become a classic example for neural computation that has attracted researchers from different fields over many decades (see also review by Clifford and Ibbotson, 2002Clifford C.W. Ibbotson M.R. Fundamental mechanisms of visual motion detection: Models, cells and functions.Prog. Neurobiol. 2002; 68: 409-437Crossref PubMed Scopus (97) Google Scholar). Focusing on the insect optic lobe and the vertebrate retina, we will provide an overview of what has been learnt about the circuits and biophysical mechanisms underlying the extraction of motion information from image sequences in different animal species. As will become evident, much progress has been made recently so that a solution seems to be within reach. Before discussing the neurons that respond specifically to the direction of a moving stimulus, we will first take a look at the problem from a computational point of view and discuss models that have been proposed to account for this computation. In physics, the velocity of a moving object is defined as the object's spatial displacement over time. For the visual detection of displacement, physical motion has to go along with changes in the spatial brightness distribution on the retina. What characterizes visual motion? Consider a smooth edge in an image moving from left to right, passing in front of a single photoreceptor (Figure 1A ). If the edge is moving slowly, the output signal will ramp up slowly, too. If the same edge is moving at a high velocity, the photoreceptor output signal will climb up steeply. Obviously, the faster the object moves, the steeper the output signal. Now consider two edges of different steepness passing by the same photoreceptor at the exact same velocity (Figure 1B): If the steep edge is moving, the output signal will again rise steeply, if the shallow edge is moving, the output signal will rise slowly. Obviously, the steeper the gradient, the steeper the output signal. Therefore, neither the speed nor the direction of the moving object can be deciphered from this output signal alone. However, both of the above dependences are captured by the following formula, relating the temporal signal change dR/dt to the product of the spatial brightness gradient dI/dx and the velocity dx/dt (Limb and Murphy, 1975Limb J.O. Murphy J.A. Estimating the velocity of moving images in television signals.Computer Graphics and Image Processing. 1975; 4: 311-327Crossref Google Scholar, Fennema and Thompson, 1979Fennema C.L. Thompson W.B. Velocity determination in scenes containing several moving objects.Computer Graphics and Image Processing. 1979; 9: 301-315Crossref Scopus (242) Google Scholar):dRdt=dIdx∗dxdtThe velocity dx/dt can, thus, be recovered by dividing the temporal change dR/dt by the spatial gradient dI/dx. Several models have been proposed in the past that calculate the direction of motion from the brightness changes as captured by the photoreceptors. The gradient model (Figure 1C) describes the most straightforward way to implement a motion detector with the above mentioned mathematical relationship. Here, the spatial gradient dI/dx is approximated by the brightness difference dI, of the pattern, I, sampled at two neighboring image points separated by a distance, dx. Both input signals become high-pass filtered, approximating the temporal derivative, and then added together. These two quantities are then divided by each other yielding an estimate of the local image velocity (Srinivasan, 1990Srinivasan M.V. Generalized gradient schemes for the measurement of two-dimensional image motion.Biol. Cybern. 1990; 63: 421-431Crossref PubMed Google Scholar). This estimate will only depend on the image velocity and not on the spatial structure of the moving pattern because the local image contrast is expressed in a steeper spatial, as well as in a steeper temporal gradient: Dividing them leads to a cancellation of image contrast. However, as attractive as the gradient model of motion detection might appear, most models that were proposed to account for biological motion detectors actually do not calculate the spatial and the temporal gradient of the moving image. They rather correlate the brightness values measured at two adjacent image points with each other after one of them has been filtered in time (correlation model, Figure 1D). Consequently, their output is not proportional to image motion but rather deviates from it in a characteristic way. In fact, this deviation has been the crucial hint for researchers in motion vision to propose exactly this type of model. The first correlation detector was proposed on the basis of experimental studies on the optomotor behavior of insects (Hassenstein and Reichardt, 1956Hassenstein B. Reichardt W. Systemtheoretische Analyse der Zeit-, Reihenfolgen- und Vorzeichenauswertung bei der Bewegungsperzeption des Riisselkiifers Chlorophanus.Z Naturforsch. 1956; IIb: 513-524Google Scholar, Reichardt, 1961Reichardt W. Autocorrelation, a principle for the evaluation of sensory information by the central nervous system.in: Rosenblith W.A. Sensory Communication. MlT Press and Wiley, New York, London1961: 303-317Google Scholar, Reichardt, 1987Reichardt W. Evaluation of optical motion information by movement detectors.J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 1987; 161: 533-547Crossref PubMed Scopus (123) Google Scholar). This correlation detector is commonly referred to as the Reichardt detector (van Santen and Sperling, 1985van Santen J.P.H. Sperling G. Elaborated Reichardt detectors.J. Opt. Soc. Am. A. 1985; 2: 300-321Crossref PubMed Google Scholar), and has also been applied to explain motion detection in different vertebrate species including man (for review, see Borst and Egelhaaf, 1989Borst A. Egelhaaf M. Principles of visual motion detection.Trends Neurosci. 1989; 12: 297-306Abstract Full Text PDF PubMed Scopus (253) Google Scholar). Such a detector consists of two mirror-symmetrical subunits. In each subunit, the signals derived from two neighboring inputs are multiplied with each other after one of them has been shifted in time by a temporal low-pass filter. The final detector response is given by the difference of the output signals. Various elaborations of the basic Reichardt model have been proposed to accommodate this motion detection scheme to perform in a species-specific way. Perhaps the simplest correlation-type movement detector has been proposed by Barlow and Levick to explain their experimental findings on DS ganglion cells in the rabbit retina (Barlow and Levick, 1965Barlow H.B. Levick W.R. The mechanism of directionally selective units in rabbit's retina.J. Physiol. 1965; 178: 477-504Crossref PubMed Google Scholar). The Barlow-Levick model (Figure 1E) is almost identical with respect to its layout but with only one subunit of the basic Reichardt model. It consists of two input lines carrying the brightness signals which are compared after one of the signals has been delayed. In contrast to the Reichardt model, this comparison is accomplished by a specific logical operation, an AND-NOT or veto gate, suppressing the detector's activity when the delay line is activated first and, consequently, both signals arrive simultaneously at the AND-NOT gate. The corresponding direction of motion, i.e., from left to right, is, therefore, the detector's null direction. For motion in the detector's preferred direction the veto signal arrives too late to have an effect. Another model which is often applied to human psychophysics and motion-sensitive neurons in the mammalian cortex is the so-called motion energy model (Adelson and Bergen, 1985Adelson E.H. Bergen J.R. Spatiotemporal energy models for the perception of motion.J. Opt. Soc. Am. A. 1985; 2: 284-299Crossref PubMed Google Scholar). Interestingly, if the Reichardt model is equipped with the same spatial and temporal filters in its input channels, it assumes the same specific functional characteristics as the energy model and even is mathematically equivalent (van Santen and Sperling, 1985van Santen J.P.H. Sperling G. Elaborated Reichardt detectors.J. Opt. Soc. Am. A. 1985; 2: 300-321Crossref PubMed Google Scholar, Adelson and Bergen, 1985Adelson E.H. Bergen J.R. Spatiotemporal energy models for the perception of motion.J. Opt. Soc. Am. A. 1985; 2: 284-299Crossref PubMed Google Scholar). This identity, however, only holds for the final, fully opponent output signal of both detectors and does not pertain to its internal structure. Despite many differences in detail, all models of motion detection share the following commonalities: (1) they all have at least two spatially separated input lines that read the brightness levels of adjacent pixels in the image, (2) they all have some sort of asymmetry with respect to the temporal filtering of the input (a temporal derivative in case of the gradient detector, a low-pass filter in one of the input channels of the Reichardt detector, a delay line in the Barlow-Levick model), and (3) they all possess an essential nonlinearity (division in the gradient detector, a multiplication in the Reichardt detector, and an AND-NOT gate in the Barlow-Levick model). They differ, however, in many other aspects that can be used to discriminate between them experimentally. (1) As a characteristic hallmark, the gradient detector delivers a signal that is proportional to image velocity independent of the local image contrast. (2) The output of the Reichardt detector grows quadratically with image contrast. Furthermore, it displays a maximum at a certain image velocity. The optimum velocity is proportional to the spatial pattern wavelength such that the maximum response is always at the same temporal frequency (image velocity divided by pattern wavelength). (3) The Barlow-Levick model is characterized by a null-direction inhibition. For an experimental analysis, it is also important to make the distinction between the response properties of the individual local motion detector, and those of a spatially integrated detector array. When stimulated by a periodic grating moving at a constant velocity, the local gradient detector will signal a constant value as well. In contrast, the output signal of a local Reichardt detector will consist of two parts: a constant DC shift that is DS and, superimposed, a periodic modulation with the local brightness of the pattern. Only when the summed output of an array of Reichardt detectors is considered, these local modulations will disappear since they are phase-shifted with respect to each other. This also holds true for the Barlow-Levick model. Neurons responding differently to visual stimuli moving in opposite directions are called DS. Such neurons have long been known to exist in the visual system and other parts of the vertebrate and invertebrate nervous system. In invertebrates, the first DS neurons were found in flies, located in a brain structure called the lobula plate. The lobula plate is the third of a stack of neuropiles of the fly's optic lobe, each forming a retinotopic representation of the image as initially formed by the compound eye. Starting from the periphery, these are called lamina, medulla, and lobula complex, the latter being divided into an anterior lobula and a posterior lobula plate (Figure 2A ). As a consequence of the retinotopic structure, each neuropile is built from repetitive columns containing an identical set of neurons first described anatomically by Ramón y Cajal on the basis of Golgi staining (Cajal and Sanchez, 1915Cajal S.R. Sanchez D. Contribucion al Conocimiento de los Centros Nerviosos de los Insectos. Imprenta de Hijos de Nicholas Moja, Madrid1915Crossref Google Scholar). For the fruit fly Drosophila melanogaster, a large set of columnar neurons has been cataloged (Fischbach and Dittrich, 1989Fischbach K.F. Dittrich A.P.M. The optic lobe of Drosophila melanogaster. I. A Golgi analysis of wild-type structure.Cell Tissue Res. 1989; 258: 441-475Crossref Google Scholar). More recently, this set has been complemented by assigning transmitter systems to various columnar neurons (e.g., Morante and Desplan, 2008Morante J. Desplan C. The color-vision circuit in the medulla of Drosophila.Curr. Biol. 2008; 18: 553-565Abstract Full Text Full Text PDF PubMed Scopus (68) Google Scholar, Raghu and Borst, 2011Raghu S.V. Borst A. Candidate glutamatergic neurons in the visual system of Drosophila.PLoS ONE. 2011; 6: e19472Crossref PubMed Scopus (16) Google Scholar, Raghu et al., 2011Raghu S.V. Reiff D.F. Borst A. Neurons with cholinergic phenotype in the visual system of Drosophila.J. Comp. Neurol. 2011; 519: 162-176Crossref PubMed Scopus (13) Google Scholar). Each columnar neuron, whether located in the lamina, medulla, or lobula complex, has distinct arborizations in particular layers of its neuropile and some neurons connecting the lamina with the medulla or the medulla to the lobula plate. Furthermore, all these cells restrict their arborizations to a small part of their respective neuropile, mostly respecting the columnar borders. This is different for the lobula plate, where dendrites of the so-called lobula plate tangential cells span large parts of the neuropile, apparently collecting signals from local neurons within hundreds of columns. These tangential cells have been thoroughly analyzed, first in the blow fly Calliphora (Hausen, 1982aHausen K. Motion sensitive interneurons in the optomotor system of the fly. I. The horizontal cells: Structure and signals.Biol. Cybern. 1982; 45: 143-156Crossref Google Scholar, Hausen, 1982bHausen K. Motion sensitive interneurons in the optomotor system of the fly. II. The horizontal cells: Receptive field organization and response characteristics.Biol. Cybern. 1982; 46: 67-79Crossref Google Scholar, Hengstenberg, 1982Hengstenberg R. Common visual response properties of giant vertical cells in the lobula plate of the blowfly Calliphora.J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 1982; 149: 179-193Crossref Google Scholar, Hengstenberg et al., 1982Hengstenberg R. Hausen K. Hengstenberg B. The number and structure of giant vertical cells (VS) in the lobula plate of the blowfly Calliphora erytrocephala.J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 1982; 149: 163-177Crossref Google Scholar, Borst and Haag, 1996Borst A. Haag J. The intrinsic electrophysiological characteristics of fly lobula plate tangential cells: I. Passive membrane properties.J. Comput. Neurosci. 1996; 3: 313-336Crossref PubMed Scopus (71) Google Scholar, Haag et al., 1997Haag J. Theunissen F. Borst A. The intrinsic electrophysiological characteristics of fly lobula plate tangential cells: II. Active membrane properties.J. Comput. Neurosci. 1997; 4: 349-369Crossref PubMed Scopus (50) Google Scholar, Haag et al., 1999Haag J. Vermeulen A. Borst A. The intrinsic electrophysiological characteristics of fly lobula plate tangential cells: III. Visual response properties.J. Comput. Neurosci. 1999; 7: 213-234Crossref PubMed Scopus (40) Google Scholar) and, more recently, also in the fruit fly Drosophila (Joesch et al., 2008Joesch M. Plett J. Borst A. Reiff D.F. Response properties of motion-sensitive visual interneurons in the lobula plate of Drosophila melanogaster.Curr. Biol. 2008; 18: 368-374Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar, Schnell et al., 2010Schnell B. Joesch M. Forstner F. Raghu S.V. Otsuna H. Ito K. Borst A. Reiff D.F. Processing of horizontal optic flow in three visual interneurons of the Drosophila brain.J. Neurophysiol. 2010; 103: 1646-1657Crossref PubMed Scopus (44) Google Scholar). Although the exact number depends on the species, the tangential cells comprise roughly 50 neurons, each of which can be uniquely identified on the basis of its anatomy, receptive field, and electrical response properties. All tangential cells respond to visual motion in a DS way. Among them, the three cells of the horizontal system, called HS cells, respond most strongly to horizontal image motion: When the pattern moves from the front to the back, the cells depolarize (Figure 2B). This direction of image motion is their preferred direction. When the pattern moves from the back to the front, they hyperpolarize. This direction of image motion is their null direction. When stimulated by a moving bar instead of a grating, their preferred and null direction remains the same, no matter whether a white bar is moving a black background or a black bar on a white background (Figure 2C). Another prominent group, the cells of the vertical system, called VS cells, in general respond most strongly to vertical image motion; downward is their preferred direction and upward is their null direction. However, precise mapping of the cells' local preferred directions revealed a spatially nonuniform receptive field; the different preferred directions in different parts of the fly's visual field resemble an optic flow pattern as might be elicited by the fly during certain flight maneuvers (Krapp and Hengstenberg, 1996Krapp H.G. Hengstenberg R. Estimation of self-motion by optic flow processing in single visual interneurons.Nature. 1996; 384: 463-466Crossref PubMed Scopus (195) Google Scholar, Krapp et al., 1998Krapp H.G. Hengstenberg B. Hengstenberg R. Dendritic structure and receptive-field organization of optic flow processing interneurons in the fly.J. Neurophysiol. 1998; 79: 1902-1917PubMed Google Scholar). These large and elaborate receptive fields could be shown to result from a combination of direct feed-forward input the tangential cells receive from columnar motion-sensitive elements and lateral synaptic interactions between the various tangential cells within the lobula plate (Borst and Weber, 2011Borst A. Weber F. Neural action fields for optic flow based navigation: A simulation study of the fly lobula plate network.PLoS ONE. 2011; 6: e16303Crossref PubMed Scopus (10) Google Scholar; for review, see Borst et al., 2010Borst A. Haag J. Reiff D.F. Fly motion vision.Annu. Rev. Neurosci. 2010; 33: 49-70Crossref PubMed Scopus (119) Google Scholar). As for the nature of their retinotopic input elements, the lobula plate tangential cells have been subjected to numerous tests investigating whether they conform to the Reichardt model in blow flies, hover flies, and fruit flies. In these experiments, tangential cells were stimulated by periodic gratings moving at a constant velocity (Haag et al., 2004Haag J. Denk W. Borst A. Fly motion vision is based on Reichardt detectors regardless of the signal-to-noise ratio.Proc. Natl. Acad. Sci. USA. 2004; 101: 16333-16338Crossref PubMed Scopus (68) Google Scholar, Joesch et al., 2008Joesch M. Plett J. Borst A. Reiff D.F. Response properties of motion-sensitive visual interneurons in the lobula plate of Drosophila melanogaster.Curr. Biol. 2008; 18: 368-374Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar, Schnell et al., 2010Schnell B. Joesch M. Forstner F. Raghu S.V. Otsuna H. Ito K. Borst A. Reiff D.F. Processing of horizontal optic flow in three visual interneurons of the Drosophila brain.J. Neurophysiol. 2010; 103: 1646-1657Crossref PubMed Scopus (44) Google Scholar) or with a dynamic velocity profile (Egelhaaf and Reichardt, 1987Egelhaaf M. Reichardt W. Dynamic response properties of movement detectors: Theoretical analysis and electrophysiological investigation in the visual system of the fly.Biol. Cybern. 1987; 56: 69-87Crossref Scopus (53) Google Scholar, Egelhaaf and Borst, 1989Egelhaaf M. Borst A. Transient and steady-state response properties of movement detectors.J. Opt. Soc. Am. A. 1989; 6: 116-127Crossref PubMed Google Scholar, Borst et al., 2003Borst A. Reisenman C. Haag J. Adaptation of response transients in fly motion vision. II: Model studies.Vision Res. 2003; 43: 1309-1322Crossref PubMed Scopus (44) Google Scholar, Reisenman et al., 2003Reisenman C. Haag J. Borst A. Adaptation of response transients in fly motion vision. I: Experiments.Vision Res. 2003; 43: 1291-1307Crossref PubMed Scopus (22) Google Scholar, Borst et al., 2005Borst A. Flanagin V.L. Sompolinsky H. Adaptation without parameter change: Dynamic gain control in motion detection.Proc. Natl. Acad. Sci. USA. 2005; 102: 6172-6176Crossref PubMed Scopus (62) Google Scholar, Spavieri et al., 2010Spavieri Jr., D.L. Eichner H. Borst A. Coding efficiency of fly motion processing is set by firing rate, not firing precision.PLoS Comput. Biol. 2010; 6: e1000860Crossref PubMed Scopus (3) Google Scholar). Some studies investigated the local motion response by restricting the field of view to a small window through which the pattern was shown to the fly (Egelhaaf et al., 1989Egelhaaf M. Borst A. Reichardt W. Computational structure of a biological motion-detection system as revealed by local detector analysis in the fly's nervous system.J. Opt. Soc. Am. A. 1989; 6: 1070-1087Crossref PubMed Google Scholar) or by using intracellular calcium concentration changes as a readout for local activity in the dendrite (Single and Borst, 1998Single S. Borst A. Dendritic integration and its role in computing image velocity.Science. 1998; 281: 1848-1850Crossref PubMed Scopus (133) Google Scholar, Haag et al., 2004Haag J. Denk W. Borst A. Fly motion vision is based on Reichardt detectors regardless of the signal-to-noise ratio.Proc. Natl. Acad. Sci. USA. 2004; 101: 16333-16338Crossref PubMed Scopus (68) Google Scholar). Tangential cells were also stimulated by natural images (Dror et al., 2001Dror R.O. O'Carroll D.C. Laughlin S.B. Accuracy of velocity estimation by Reichardt correlators.J. Opt. Soc. Am. A Opt. Image Sci. Vis. 2001; 18: 241-252Crossref PubMed Google Scholar) or by apparent motion stimuli consisting of spatially displaced sequences of discrete brightness steps (Egelhaaf and Borst, 1992Egelhaaf M. Borst A. Are there separate ON and OFF channels in fly motion vision?.Vis. Neurosci. 1992; 8: 151-164Crossref PubMed Google Scholar). All these studies concluded that the Reichardt detector accurately describes the behavior of these input elements. As an example, the responses of Drosophila HS cells have been measured as a function of pattern contrast (Figure 2D): Although the response does not rise quadratically as predicted by a perfect multiplication, it clearly increases with increasing pattern contrast, thus ruling out a division of temporal by spatial gradient as specified in the gradient detector. When stimulated by a periodic grating drifting at different velocities, the response of HS cells displays a velocity optimum, as predicted by the Reichardt detector (Figure 2E, black trace). Furthermore, when the test is repeated with a grating of twice the spatial wavelength, the optimum velocity is doubled (Figure 2E, gray trace). When the pattern velocity is divided by the spatial wavelength of the pattern, both curves coincide, revealing a peak at the same temporal frequency of 1 Hz (Figure 2F), exactly as predicted by the Reichardt detector. The first reports of DS neurons in the vertebrate retina appeared in the 1960s (for references see Wyatt and Daw, 1975Wyatt H.J. Daw N.W. Directionally sensitive ganglion cells in the rabbit retina: Specificity for stimulus direction, size, and speed.J. Neurophysiol. 1975; 38: 613-626PubMed Google Scholar). In particular, an elegant series of papers by Barlow, Levick, and coworkers (e.g., Barlow and Hill, 1963Barlow H.B. Hill R.M. Selective sensitivity to direction of movement in ganglion cells of the rabbit retina.Science. 1963; 139: 412-414Crossref PubMed Google Scholar, Barlow et al., 1964Barlow H.B. Hill R.M. Levick W.R. Rabbit retinal ganglion cells responding selectively to direction and speed of image motion in the rabbit.J. Physiol. 1964; 173: 377-407Crossref PubMed Google Scholar, Barlow and Levick, 1965Barlow H.B. Levick W.R. The mechanism of directionally selective units in rabbit's retina.J. Physiol. 1965; 178: 477-504Crossref PubMed Google Scholar) on DS ganglion cells in the rabbit retina initiated more than 40 years of research that established the retinal DS circuitry as one of the most investigated and best understood neuronal circuitries in the vertebrate brain. The first type of retinal DS ganglion cells fires both at the leading and the trailing edge of a stimulus moving along the preferred direction through the receptive field (Barlow and Levick, 1965Barlow H.B. Levick W.R. The mechanism of directionally selective units in rabbit's retina.J. Physiol. 1965; 178: 477-504Crossref PubMed Google Scholar). In other words, a bright spot on a dark background evoked very similar DS responses as a dark spot on a bright background. Due to this contrast independence, this cell type is referred to as ON/OFF DS ganglion cell (for review, see Masland, 2004Masland R.H. Direction Selectivity in Retinal Ganglion Cells.in: Chalupa L.M. Werner J.S. The Visual Neurosciences. The MIT Press, Cambridge, MA2004: 451-462Google Scholar, Vaney et al., 2001Vaney D.I. He S. Taylor W.R. Levick W.R. Direction-Selective Ganglion Cells in the Retina.in: Zanker J.M. Zeil J. Motion Vision - Computational, Neural, and Ecological Constraints. Springer, Berlin, Heidelberg, New York2001: 13-56Crossref Google Scholar). They have a distinct morphology with loopy dendrites (Figure 3A ; Amthor et al., 1984Amthor F.R. Oyster C.W. Takahashi E.S. Morphology of on-off direction-selective ganglion cells in the rabbit retina.Brain Res. 1984; 298: 187-190Crossref PubMed Scopus (120) Google Scholar, Amthor et al., 1989Amthor F.R. Takahashi E.S. Oyster C.W. Morphologies of rabbit retinal ganglion cells with complex receptive fields.J. Comp. Neurol. 1989; 280: 97-121Crossref PubMed Google Scholar) ramifying in both the ON and the OFF sublamina of the inner plexiform layer (IPL) (Figure 3D, red cell). The two arborizations can differ in size and shape (Oyster et al., 1993Oyster C.W. Amthor F.R. Takahashi E.S. Dendritic architecture of ON-OFF direction-selective ganglion cells in the rabbit retina.Vision Res. 1993; 33: 579-608Crossref PubMed Scopus (44) Google Scholar, Vaney, 1994Vaney D.I. Territorial organization of direction-selective ganglion cells in rabbit retina.J. Neurosci. 1994; 14: 6301-6316PubMed Google Scholar), suggesting that the ON and the OFF DS circuits work independently. ON/OFF DS ganglion cells are inhibited by synchronous motion outside their receptive field center and are, thus, sensitive to motion contrast (Chiao and Masland, 2003Chiao C.C. Masland R.H. Contextual tuning of direction-selective retinal ganglion cells.Nat. Neurosci. 2003; 6: 1251-1252Crossref PubMed Scopus (15) Google Scholar). As a result of their response properties, ON/OFF ganglion cells are considered to be local motion detectors. They display a rather broad tuning in both the temporal and spatial frequency domain (see e.g., Figure 2 in Grzywacz and Amthor, 2007Grzywa" @default.
• W1978220087 created "2016-06-24" @default.
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• W1978220087 date "2011-09-01" @default.
• W1978220087 modified "2023-10-14" @default.
• W1978220087 title "Seeing Things in Motion: Models, Circuits, and Mechanisms" @default.
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