Disparity-tuned cells in primary visual cortex (VI) are thought to play a significant role in the processing of stereoscopic depth. The disparity-specific responses of these neurons have been previously described by an energy model based on local, feedforward interactions. This model fails to predict the response to binocularly anticorrelated stimuli, in which images presented to left and right eyes have opposite contrasts. The original energy model predicts that anticorrelation should invert the disparity tuning curve (phase difference pi), with no change in the amplitude of the response. Experimentally, the amplitude tends to be reduced with anticorrelated stimuli and a spread of phase differences is observed, although phase differences near pi are the most common. These experimental observations could potentially reflect a modulation of the V1 signals by feedback from higher visual areas (because anticorrelated stimuli create a weaker or nonexistent stereoscopic depth sensation). This hypothesis could explain the effects on amplitude, but the spread of phase differences is harder to understand. Here, we demonstrate that changes in both amplitude and phase can be explained by a straightforward modification of the energy model that involves only local processing. Input from each eye is passed through a monocular simple cell, incorporating a threshold, before being combined at a binocular simple cell that feeds into the energy computation. Since this local feedforward model can explain the responses of complex cells to both correlated and anticorrelated stimuli, there is no need to invoke any influence of global stereoscopic matching.

A fundamental challenge of binocular vision is that objects project to different positions on the two retinas (binocular disparity). Neurons in visual cortex show two distinct types of tuning to disparity: position and phase disparity, due to differences in receptive field location and profile respectively. Here, we point out that phase disparity does not occur in natural images. Why, then, should the brain encode it? We propose that phase disparity detectors help work out which feature in the left eye corresponds to a given feature in the right. This correspondence problem is bedeviled by false matches: regions of the image which look similar but do not correspond to the same object. We show that phase-disparity neurons tend to be more strongly activated by false matches. Thus, they may act as "lie detectors", enabling the true correspondence to be deduced by a process of elimination.Over the past 35 years, neurophysiologists have mapped the response properties of binocular neurons in primary visual cortex and elsewhere in considerable detail. A mathematical model, the stereo energy model 1 , has been developed, which successfully describes many of their properties. Within this model, binocular neurons can encode disparity in two basic ways: phase disparity (Fig. 1a), in which receptive fields differ in the arrangement of their ON and OFF regions, but not in retinal position, and position disparity (Fig. 1b), in which left and right-eye receptive fields differ in their position on the retina but not in their profile 2 . Several recent studies in V1 3-6 have concluded that most disparity-selective cells are hybrid 7 , showing both position and phase disparity. These neurophysiological data present a challenge to computational models of stereopsis. Why does the brain devote computational resources to encoding disparity twice over, once through position and once through phase?Phase disparity presents a particular puzzle, since it does not correspond to anything experienced in natural viewing. For a surface such as a wall in front of the observer, where disparity is locally uniform, the two eyes' images of a given patch on the surface are related by a simple position shift on the retina (Fig. 1b). For an inclined surface, with a linear disparity gradient, the two image patches are also compressed and/or rotated with respect to one another: i.e., they differ in spatial frequency and/or orientation (Fig. 1c). Higher-order changes in disparity, such as produced by curved surfaces, produce images whose spatial frequency and orientation differences vary across the retina (Fig. 1d 8 ). Disparity discontinuities, which occur at object boundaries, produce different disparities in different regions of the retina 9 . However, phase disparity neurons do not appear to be constructed to detect any of these possible situations. They respond optimally to stimuli in which the left and right eye's image are related by a constant shift in Fourier phase, i.e. each Fourier component is displaced by an amount proportional to it...

Stereopsis - 3D vision – has become widely used as a model of perception. However, all our knowledge of possible underlying mechanisms comes almost exclusively from vertebrates. While stereopsis has been demonstrated for one invertebrate, the praying mantis, a lack of techniques to probe invertebrate stereopsis has prevented any further progress for three decades. We therefore developed a stereoscopic display system for insects, using miniature 3D glasses to present separate images to each eye, and tested our ability to deliver stereoscopic illusions to praying mantises. We find that while filtering by circular polarization failed due to excessive crosstalk, “anaglyph” filtering by spectral content clearly succeeded in giving the mantis the illusion of 3D depth. We thus definitively demonstrate stereopsis in mantises and also demonstrate that the anaglyph technique can be effectively used to deliver virtual 3D stimuli to insects. This method opens up broad avenues of research into the parallel evolution of stereoscopic computations and possible new algorithms for depth perception.

Read, Jenny C. A. and Bruce G. Cumming. Testing quantitative models of binocular disparity selectivity in primary visual cortex. J Neurophysiol 90: 2795-2817, 2003. First published July 16, 2003 10.1152/jn.01110.2002. Disparity-selective neurons in striate cortex (V1) probably implement the initial processing that supports binocular vision. Recently, much progress has been made in understanding the computations that these neurons perform on retinal inputs. The binocular energy model has been highly successful in providing a simple theory of these computations. A key feature of the energy model is that it is linear until after inputs from the two eyes are combined. Recently, however, a modified version of the energy model, incorporating threshold nonlinearities before binocular combination, has been proposed to account for the weaker disparity tuning observed with anticorrelated stimuli. In this study, we present new data needed for a critical assessment of these two models. We compare two key predictions of the models with responses of disparity-selective neurons recorded from V1 of awake fixating monkeys. We find that the original energy model, and a family of generalizations retaining linear binocular combination, are quantitatively inconsistent with the response of V1 neurons. In contrast, the modified version incorporating threshold nonlinearities can explain both sets of observations. We conclude that the energy model can be reconciled with experimental observations by adding a threshold before binocular combination. This gives us the clearest picture yet of the computation being carried out by disparity-selective V1 neurons. I N T R O D U C T I O NThe separation of the two eyes introduces disparities between the images received by the left and right eyes. The visual system is somehow able to fuse the images so as to produce a unified percept of the visual world, while using the stereoscopic disparities to extract information about how far away viewed objects are. The neural circuits specific to this ability begin in primary visual cortex (V1), the first place in the visual system where inputs from the two eyes converge on individual cells. Many V1 cells modulate their firing rate according to the stereoscopic disparity of the stimulus (Barlow et al. 1967;Nikara et al. 1968). These disparity-tuned cells are believed to perform the initial processing of retinal inputs that eventually, in higher visual areas, gives rise to stereoscopic depth perception and to binocular fusion (single vision). Thus, a detailed understanding of the computations carried out by these cells represents the first step toward a complete description of stereoscopic vision.The current best description of the operation of these cells is provided by the energy model (Adelson and Bergen 1985;Fleet et al. 1996;Ohzawa 1998;Ohzawa et al. 1990;Qian 1994), sketched in Fig. 1A and described more fully below. This elegant model has been extremely successful in explaining qualitatively the properties of disparity-tuned neurons in V1, for example...

A puzzle for neuroscience-and robotics-is how insects achieve surprisingly complex behaviours with such tiny brains. One example is depth perception via binocular stereopsis in the praying mantis, a predatory insect. Praying mantids use stereopsis, the computation of distances from disparities between the two retinal images, to trigger a raptorial strike of their forelegs when prey is within reach. The neuronal basis of this ability is entirely unknown. Here we show the first evidence that individual neurons in the praying mantis brain are tuned to specific disparities and eccentricities, and thus locations in 3D-space. Like disparity-tuned cortical cells in vertebrates, the responses of these mantis neurons are consistent with linear summation of binocular inputs followed by an output nonlinearity. Our study not only proves the existence of disparity sensitive neurons in an insect brain, it also reveals feedback connections hitherto undiscovered in any animal species.

Stereo or ‘3D’ vision is an important but costly process seen in several evolutionarily distinct lineages including primates, birds and insects. Many selective advantages could have led to the evolution of stereo vision, including range finding, camouflage breaking and estimation of object size. In this paper, we investigate the possibility that stereo vision enables praying mantises to estimate the size of prey by using a combination of disparity cues and angular size cues. We used a recently developed insect 3D cinema paradigm to present mantises with virtual prey having differing disparity and angular size cues. We predicted that if they were able to use these cues to gauge the absolute size of objects, we should see evidence for size constancy where they would strike preferentially at prey of a particular physical size, across a range of simulated distances. We found that mantises struck most often when disparity cues implied a prey distance of 2.5 cm; increasing the implied distance caused a significant reduction in the number of strikes. We, however, found no evidence for size constancy. There was a significant interaction effect of the simulated distance and angular size on the number of strikes made by the mantis but this was not in the direction predicted by size constancy. This indicates that mantises do not use their stereo vision to estimate object size. We conclude that other selective advantages, not size constancy, have driven the evolution of stereo vision in the praying mantis.This article is part of the themed issue ‘Vision in our three-dimensional world’.

Stereopsis is the computation of depth information from views acquired simultaneously from different points in space. For many years, stereopsis was thought to be confined to primates and other mammals with front-facing eyes. However, stereopsis has now been demonstrated in many other animals, including lateral-eyed prey mammals, birds, amphibians and invertebrates. The diversity of animals known to have stereo vision allows us to begin to investigate ideas about its evolution and the underlying selective pressures in different animals. It also further prompts the question of whether all animals have evolved essentially the same algorithms to implement stereopsis. If so, this must be the best way to do stereo vision, and should be implemented by engineers in machine stereopsis. Conversely, if animals have evolved a range of stereo algorithms in response to different pressures, that could inspire novel forms of machine stereopsis appropriate for distinct environments, tasks or constraints. As a first step towards addressing these ideas, we here review our current knowledge of stereo vision in animals, with a view towards outlining common principles about the evolution, function and mechanisms of stereo vision across the animal kingdom. We conclude by outlining avenues for future work, including research into possible new mechanisms of stereo vision, with implications for machine vision and the role of stereopsis in the evolution of camouflage.

At many central synapses, the presynaptic bouton and postsynaptic density are structurally correlated. However, it is unknown whether this correlation extends to the functional properties of the synapses. To investigate this, we made recordings from synaptically coupled pairs of pyramidal neurons in rat visual cortex. The mean peak amplitude of EPSPs recorded from pairs of L2/3 neurons ranged between 40 V and 2.9 mV. EPSP rise times were consistent with the majority of the synapses being located on basal dendrites; this was confirmed by full anatomical reconstructions of a subset of connected pairs. Over a third of the connections could be described using a quantal model that assumed simple binomial statistics. Release probability (P r ) and quantal size (Q), as measured at the somatic recording site, showed considerable heterogeneity between connections. However, across the population of connections, values of P r and Q for individual connections were positively correlated with one another. This correlation also held for inputs to layer 5 pyramidal neurons from both layer 2/3 and neighboring layer 5 pyramidal neurons, suggesting that during development of cortical connections presynaptic and postsynaptic strengths are dependently scaled. For 2/3 to 2/3 connections, mean EPSP amplitude was correlated with both Q and P r values but uncorrelated with N, the number of functional release sites mediating the connection. The efficacy of a cortical connection is thus set by coordinated presynaptic and postsynaptic strength.