视差检测：简单细胞、复杂细胞及能量模型

立体视觉信息的处理在于皮层双眼性细胞的活动.皮层中简单细胞对视差的编码方式被认为有两种：位置差（position shift）和相位差（phase shift）,但简单细胞并不适合作为视差检测器.对一些复杂细胞的视差响应特性的生理研究,发现复杂细胞是一种比较适合的视差检测器.模型的研究提出基于这类简单细胞的复杂细胞能量模型,可以很好的检测视差,并可以较好的解释一些生理现象.     

Receptive fields of disparity-tuned simple cells in macaque V1

Binocular simple cells in primary visual cortex (V1) are the first cells along the mammalian visual pathway to receive input from both eyes. Two models of how binocular simple cells could extract disparity information have been put forward. The phase-shift model proposes that the receptive fields in the two eyes have different subunit organizations, while the position-shift model proposes that they have different overall locations. In five fixating macaque monkeys, we recorded from 30 disparity-tuned simple cells that showed selectivity to the disparity in a random dot stereogram. High-resolution maps of the left and right eye receptive fields indicated that both phase and position shifts were common. Single cells usually showed a combination of the two, and the optimum disparity was best correlated with the sum of receptive field phase and position shift.     

A binocular model for the simple cell

We authors propose a mathematical model for simple cell binocular response. It comprises two Gabor-type receptive fields (RF) having the same RF center, preferred spatial frequency, and preferred orientation. The model integrates the equally weighted signals from both eyes and performs a threshold operation. Poggio and Fischer (1977) classified binocular disparity cells in the striate cortex into four groups: tuned excitatory (TE), tuned inhibitory (TI), near, and far cells. They also found that most of the TE cells are ocularly balanced and that the other three types are usually unbalanced. This model can imitate these four types of disparity sensitivities and their ocular dominance tendency. We perform model fittings to Poggio's data using the “simulated annealing” method and discuss parameter dependence of the model's response. The model can also respond with exceptional disparity sensitivity: i.e., flat type, alternating type, and intermediate type.     

Vergence eye movement control and multivalent perception of autostereograms

We introduce a dynamical model for automatic vergence eye movement control. In connection with our dynamical system of binocular model neurons that solves the correspondence problem of stereo-vision, we present a complete model for stereo-vision. Our automatic vergence eye movement control adjusts an image segment, which is of momentary interest to the observer. The adjustment is done in such a way that we only need to define a disparity search range of minimal extension. ecently, a new method of encoding (3D) three-dimenional information in 2D pictures was designed in the form of computer-generated patterns of colored dots. At first glimpse, these so-called autostereograms appear as structured but meaningless patterns. After a certain period of observation, a 3D pattern emerges suddenly in an impressive way. Applying our algorithm to autostereograms, we find a fully satisfactory agreement with the multivalent perception experienced by humans. As in nature, in our model the phase transition between the initial state and the 3D perception state takes place in a very short time. Our algorithm is very robust against noise, and there is no need to interpolate a sparse depth map.     

The Role of Binocular Disparity in Stereoscopic Images of Objects in the Macaque Anterior Intraparietal Area

Neurons in the macaque Anterior Intraparietal area (AIP) encode depth structure in random-dot stimuli defined by gradients of binocular disparity, but the importance of binocular disparity in real-world objects for AIP neurons is unknown. We investigated the effect of binocular disparity on the responses of AIP neurons to images of real-world objects during passive fixation. We presented stereoscopic images of natural and man-made objects in which the disparity information was congruent or incongruent with disparity gradients present in the real-world objects, and images of the same objects where such gradients were absent. Although more than half of the AIP neurons were significantly affected by binocular disparity, the great majority of AIP neurons remained image selective even in the absence of binocular disparity. AIP neurons tended to prefer stimuli in which the depth information derived from binocular disparity was congruent with the depth information signaled by monocular depth cues, indicating that these monocular depth cues have an influence upon AIP neurons. Finally, in contrast to neurons in the inferior temporal cortex, AIP neurons do not represent images of objects in terms of categories such as animate-inanimate, but utilize representations based upon simple shape features including aspect ratio.     

Stereoscopic Depth Perception Using a Model Based on the Primary Visual Cortex

This work describes an approach inspired by the primary visual cortex using the stimulus response of the receptive field profiles of binocular cells for disparity computation. Using the energy model based on the mechanism of log-Gabor filters for disparity encodings, we propose a suitable model to consistently represent the complex cells by computing the wide bandwidths of the cortical cells. This way, the model ensures the general neurophysiological findings in the visual cortex (V1), emphasizing the physical disparities and providing a simple selection method for the complex cell response. The results suggest that our proposed approach can achieve better results than a hybrid model with phase-shift and position-shift using position disparity alone.     

Fine discrimination training alters the causal contribution of macaque area MT to depth perception

When a new perceptual task is learned, plasticity occurs in the brain to mediate improvements in performance with training. How do these changes affect the neural substrates of previously learned tasks? We addressed this question by examining the effect of fine discrimination training on the causal contribution of area MT to coarse depth discrimination. When monkeys are trained to discriminate between two coarse absolute disparities (near versus far) embedded in noise, reversible inactivation of area MT devastates performance. In contrast, after animals are trained to discriminate fine differences in relative disparity, MT inactivation no longer impairs coarse depth discrimination. This effect does not result from changes in the disparity tuning of MT neurons, suggesting plasticity in the flow of disparity signals to decision circuitry. These findings show that the contribution of particular brain area to task performance can change dramatically as a result of learning new tasks.     

Biological oscillators can be stopped—Topological study of a phase response curve

Many biological oscillators are stable against noise and perturbation (e.g. circadian rhythms, biochemical oscillators, pacemaker neurons, bursting neurons and neural networks with periodic outputs). The experiment of phase shifts resulting from discrete perturbation of stable biological rhythms was developed by Perkel and coworkers (Perkel et al., 1964). By these methods, they could get important insights into the entrainment behaviors of biological rhythms. Phase response curves, which are measured in these experiments, can be classified into two types. The one is the curve with one mapping degree (Type 1), and the other is that with zero mapping degree (Type 0) (Winfree, 1970). We define the phase response curve mathematically, and explain the difference between these two types by the homotopy theory. Moreover, we prove that, if a Type 0 curve is obtained at a certain magnitude of perturbation, there exists at least one lower magnitude for which the phase response curve cannot be measured. Some applications of these theoretical results are presented.     

Visual depth encoding in populations of neurons with localized receptive fields

 Stereopsis is the ability to perceive three-dimensional structure from disparities between the two-dimensional retinal images. Although disparity-sensitive neurons have been proposed as a neural representation of this ability many years ago, it is still difficult to link all qualities of stereopsis to properties of the neural correlate of binocular disparities. The present study wants to support efforts directed at closing the gap between electrophysiology and psychophysics. Populations of disparity-sensitive neurons in V1 were simulated using the energy-neuron model. Responses to different types of stimuli were evaluated with an efficient statistical estimator and related to psychophysical findings. The representation of disparity in simulated population responses appeared to be very robust. Small populations allowed good depth discrimination. Two types of energy neurons (phase- and position-type models) that are discussed as possible neural implementations of disparity-selectivity could be compared to each other. Phase-type coding was more robust and could explain a tendency towards zero disparity in degenerated stimuli and, for high-pass stimuli, exhibited the breakdown of disparity discrimination at a maximum disparity value. Contrast-inverted stereograms led to high variances in disparity representation, which is a possible explanation of the absence of depth percepts in large contrast-inverted stimuli. Our study suggests that nonlocal interactions destroy depth percepts in large contrast-inverted stereograms, although these percepts occur for smaller stimuli of the same class. Received: 21 December 2001 / Accepted: 29 April 2002 RID="*" ID="*" Present address: Bayer AG BTS-PT-MVT-MKM, Geb. K9, 51368 Leverkusen, Germany Acknowledgement. This work was supported by a scholarship from the Studienstiftung des deutschen Volkes to J.L. Correspondence to: J. Lippert (e-mail: joerg.lippert.jl@bayer-ag.de)     

Perceptual “Read-Out” of Conjoined Direction and Disparity Maps in Extrastriate Area MT

Cortical neurons are frequently tuned to several stimulus dimensions, and many cortical areas contain intercalated maps of multiple variables. Relatively little is known about how information is “read out” of these multidimensional maps. For example, how does an organism extract information relevant to the task at hand from neurons that are also tuned to other, irrelevant stimulus dimensions? We addressed this question by employing microstimulation techniques to examine the contribution of disparity-tuned neurons in the middle temporal (MT) visual area to performance on a direction discrimination task. Most MT neurons are tuned to both binocular disparity and the direction of stimulus motion, and MT contains topographic maps of both parameters. We assessed the effect of microstimulation on direction judgments after first characterizing the disparity tuning of each stimulation site. Although the disparity of the stimulus was irrelevant to the required task, we found that microstimulation effects were strongly modulated by the disparity tuning of the stimulated neurons. For two of three monkeys, microstimulation of nondisparity-selective sites produced large biases in direction judgments, whereas stimulation of disparity-selective sites had little or no effect. The binocular disparity was optimized for each stimulation site, and our result could not be explained by variations in direction tuning, response strength, or any other tuning property that we examined. When microstimulation of a disparity-tuned site did affect direction judgments, the effects tended to be stronger at the preferred disparity of a stimulation site than at the nonpreferred disparity, indicating that monkeys can selectively monitor direction columns that are best tuned to an appropriate conjunction of parameters. We conclude that the contribution of neurons to behavior can depend strongly upon tuning to stimulus dimensions that appear to be irrelevant to the current task, and we suggest that these findings are best explained in terms of the strategy used by animals to perform the task.     

Perceptual “Read-Out” of Conjoined Direction and Disparity Maps in Extrastriate Area MT

Cortical neurons are frequently tuned to several stimulus dimensions, and many cortical areas contain intercalated maps of multiple variables. Relatively little is known about how information is “read out” of these multidimensional maps. For example, how does an organism extract information relevant to the task at hand from neurons that are also tuned to other, irrelevant stimulus dimensions? We addressed this question by employing microstimulation techniques to examine the contribution of disparity-tuned neurons in the middle temporal (MT) visual area to performance on a direction discrimination task. Most MT neurons are tuned to both binocular disparity and the direction of stimulus motion, and MT contains topographic maps of both parameters. We assessed the effect of microstimulation on direction judgments after first characterizing the disparity tuning of each stimulation site. Although the disparity of the stimulus was irrelevant to the required task, we found that microstimulation effects were strongly modulated by the disparity tuning of the stimulated neurons. For two of three monkeys, microstimulation of nondisparity-selective sites produced large biases in direction judgments, whereas stimulation of disparity-selective sites had little or no effect. The binocular disparity was optimized for each stimulation site, and our result could not be explained by variations in direction tuning, response strength, or any other tuning property that we examined. When microstimulation of a disparity-tuned site did affect direction judgments, the effects tended to be stronger at the preferred disparity of a stimulation site than at the nonpreferred disparity, indicating that monkeys can selectively monitor direction columns that are best tuned to an appropriate conjunction of parameters. We conclude that the contribution of neurons to behavior can depend strongly upon tuning to stimulus dimensions that appear to be irrelevant to the current task, and we suggest that these findings are best explained in terms of the strategy used by animals to perform the task.     

Vertical Binocular Disparity is Encoded Implicitly within a Model Neuronal Population Tuned to Horizontal Disparity and Orientation

Primary visual cortex is often viewed as a “cyclopean retina”, performing the initial encoding of binocular disparities between left and right images. Because the eyes are set apart horizontally in the head, binocular disparities are predominantly horizontal. Yet, especially in the visual periphery, a range of non-zero vertical disparities do occur and can influence perception. It has therefore been assumed that primary visual cortex must contain neurons tuned to a range of vertical disparities. Here, I show that this is not necessarily the case. Many disparity-selective neurons are most sensitive to changes in disparity orthogonal to their preferred orientation. That is, the disparity tuning surfaces, mapping their response to different two-dimensional (2D) disparities, are elongated along the cell''s preferred orientation. Because of this, even if a neuron''s optimal 2D disparity has zero vertical component, the neuron will still respond best to a non-zero vertical disparity when probed with a sub-optimal horizontal disparity. This property can be used to decode 2D disparity, even allowing for realistic levels of neuronal noise. Even if all V1 neurons at a particular retinotopic location are tuned to the expected vertical disparity there (for example, zero at the fovea), the brain could still decode the magnitude and sign of departures from that expected value. This provides an intriguing counter-example to the common wisdom that, in order for a neuronal population to encode a quantity, its members must be tuned to a range of values of that quantity. It demonstrates that populations of disparity-selective neurons encode much richer information than previously appreciated. It suggests a possible strategy for the brain to extract rarely-occurring stimulus values, while concentrating neuronal resources on the most commonly-occurring situations.     

Disparity estimation through Green’s functions of matching equations

Binocular disparities arise from positional differences of scene features projected in the two retinae, and constitute the primary sensory cue for stereo vision. Here we introduce a new computational model for disparity estimation, based on the Green’s function of an image matching equation. When filtering a Gabor-function-modulated signal, the considered Green’s function yields a similarly modulated but shifted version of the original signal. Since a Gabor function models the receptive field of a cortical simple cell, the Green’s kernel thus allows the simulation of relative shifts between the cell’s left and right binocular inputs. A measure of the local degree of matching of such shifted inputs can then be introduced which affords disparity estimation in a similar manner to the energy model of the complex cortical cells. We have therefore effectively reformulated, in physiologically plausible terms, an image matching approach to disparity estimation. Our experiments show that the Green’s function method allows the detection of disparities both from random-dot and real-world stereograms. Partially supported by CNPq-Brazil.     

Shading Beats Binocular Disparity in Depth from Luminance Gradients: Evidence against a Maximum Likelihood Principle for Cue Combination

Perceived depth is conveyed by multiple cues, including binocular disparity and luminance shading. Depth perception from luminance shading information depends on the perceptual assumption for the incident light, which has been shown to default to a diffuse illumination assumption. We focus on the case of sinusoidally corrugated surfaces to ask how shading and disparity cues combine defined by the joint luminance gradients and intrinsic disparity modulation that would occur in viewing the physical corrugation of a uniform surface under diffuse illumination. Such surfaces were simulated with a sinusoidal luminance modulation (0.26 or 1.8 cy/deg, contrast 20%-80%) modulated either in-phase or in opposite phase with a sinusoidal disparity of the same corrugation frequency, with disparity amplitudes ranging from 0’-20’. The observers’ task was to adjust the binocular disparity of a comparison random-dot stereogram surface to match the perceived depth of the joint luminance/disparity-modulated corrugation target. Regardless of target spatial frequency, the perceived target depth increased with the luminance contrast and depended on luminance phase but was largely unaffected by the luminance disparity modulation. These results validate the idea that human observers can use the diffuse illumination assumption to perceive depth from luminance gradients alone without making an assumption of light direction. For depth judgments with combined cues, the observers gave much greater weighting to the luminance shading than to the disparity modulation of the targets. The results were not well-fit by a Bayesian cue-combination model weighted in proportion to the variance of the measurements for each cue in isolation. Instead, they suggest that the visual system uses disjunctive mechanisms to process these two types of information rather than combining them according to their likelihood ratios.     

Occlusion junctions do not improve stereoacuity

Occlusion geometry gives rise to interocular shifts in the positions of binocularly viewed contour junctions. Since these shifts do not give rise to normal binocular disparities, they have been called 'pseudodisparities'. Previous work has shown that the unmatched contour segments of a partially occluded contour at occlusion junctions can be used to recover the geometry of the occluding surface through the construction of 'illusory' contours. Here, experiments were performed to determine whether such junction shifts could enhance stereoscopic depth detection when the relative disparity between the contours was below threshold. Our results showed that stereoscopic depth detection does not improve when pseudodisparity is present. We conclude that the visual system is less sensitive to pseudodisparity than to conventional disparity information. We suggest that the primary role of pseudodisparity is to overcome conditions of camouflage.     

Circadian Activity Rhythms and Phase‐Shifting of Cultured Neurons of the Rat Suprachiasmatic Nucleus

The mammalian suprachiasmatic nucleus (SCN) is the major endogenous pacemaker that coordinates various daily rhythms including locomotor activity and autonomous and endocrine responses, through a neuronal and humoral influence. In the present study we examined the behavior of dispersed individual SCN neurons obtained from 1‐ to 3‐day‐old rats cultured on multi‐microelectrode arrays (MEAs). SCN neurons were identified by immunolabeling for the neuropeptides arginine‐vasopressin (AVP) and vasoactive intestinal polypeptide (VIP). Single SCN neurons cultured at low density onto an MEA can express firing rate patterns with different circadian phases. In these cultures we observed rarely synchronized firing patterns on adjacent electrodes. This suggests that, in cultures of low cell densities, SCN neurons function as independent pacemakers. To investigate whether individual pacemakers can be influenced independently by phase‐shifting stimuli, we applied melatonin (10 pM to 100 nM) for 30 min at different circadian phases and continuously monitored the firing rate rhythms. Melatonin could elicit phase‐shifting responses in individual clock cells which had no measurable input from other neurons. In several neurons, phase‐shifts occurred with a long delay in the second or third cycle after melatonin treatment, but not in the first cycle. Phase‐shifts of isolated SCN neurons were also observed at times when the SCN showed no sensitivity to these phase‐shifting stimuli in recordings from brain slices. This finding suggests that the neuronal network plays an essential role in the control of phase‐shifts.     

The Problem of Colliding Networks and its Relation to Cell Fusion and?Cancer

Cell fusion, a process that merges two or more cells into one, is required for normal development and has been explored as a tool for stem cell therapy. It has also been proposed that cell fusion causes cancer and contributes to its progression. These functions rely on a poorly understood ability of cell fusion to create new cell types. We suggest that this ability can be understood by considering cells as attractor networks whose basic property is to adopt a set of distinct, stable, self-maintaining states called attractors. According to this view, fusion of two cell types is a collision of two networks that have adopted distinct attractors. To learn how these networks reach a consensus, we model cell fusion computationally. To do so, we simulate patterns of gene activities using a formalism developed to simulate patterns of memory in neural networks. We find that the hybrid networks can assume attractors that are unrelated to parental attractors, implying that cell fusion can create new cell types by nearly instantaneously moving cells between attractors. We also show that hybrid networks are prone to assume spurious attractors, which are emergent and sporadic network states. This finding means that cell fusion can produce abnormal cell types, including cancerous types, by placing cells into normally inaccessible spurious states. Finally, we suggest that the problem of colliding networks has general significance in many processes represented by attractor networks, including biological, social, and political phenomena.     

A model of neural network extracting binocular parallax

A model of neural network extracting binocular parallax is proposed. It is a multilayered network composed of analog threshold elements. Three types of binocular neurons are included in this model. They are binocular simple neurons, binocular gate neurons and binocular depth neurons. The final layers of this model consist of elements which correspond to the binocular depth neurons. The performance of the model has been simulated on a digital computer. The results of the computer simulation show that every element of this model acts like neurons found in cat's and monkey's visual system and this model extracts binocular parallax caused by simple line components satisfactorily.     

Efficient Transmission of Subthreshold Signals in Complex Networks of Spiking Neurons

We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances—that naturally balances the network with excitatory and inhibitory synapses—and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.