一种感知视觉运动信息的简化脑模型

视觉运动信息的感知过程,包括从局域运动检测到对模式整体运动的感知过程.我们以蝇视觉系统的图形-背景相对运动分辨的神经回路网络为基本框架,采用初级运动检测器的六角形阵列作为输入层,构造了一种感知视觉运动信息的简化脑模型,模拟了运动信息应该神经计算模型各个层次上的处理.该模型对差分行为实验结果作出了正确预测.本文并对空间生理整合的神经机制作了讨论.     

Dynamic response properties of movement detectors: Theoretical analysis and electrophysiological investigation in the visual system of the fly

Dynamic aspects of the computation of visual motion information are analysed both theoretically and experimentally. The theoretical analysis is based on the type of movement detector which has been proposed to be realized in the visual system of insects (e.g. Hassenstein and Reichardt 1956; Reichardt 1957, 1961; Buchner 1984), but also of man (e.g. van Doorn and Koenderink 1982a, b; van Santen and Sperling 1984; Wilson 1985). The output of both a single movement detector and a one-dimensional array of detectors is formulated mathematically as a function of time. The resulting movement detector theory can be applied to a much wider range of moving stimuli than has been possible on the basis of previous formulations of the detector output. These stimuli comprise one-dimensional smooth detector input functions, i.e. functions which can be expanded into a time-dependent convergent Taylor series for any value of the spatial coordinate.The movement detector response can be represented by a power series. Each term of this series consists of one exclusively time-dependent component and of another component that depends, in addition, on the properties of the pattern. Even the exclusively time-dependent components of the movement detector output are not solely determined by the stimulus velocity. They rather depend in a non-linear way on the weighted sum of the instantaneous velocity and all its higher order time derivatives. The latter point represents another reason — not discussed so far in the literature — that movement detectors of the type analysed here do not represent pure velocity sensors.The significance of this movement detector theory is established for the visual system of the fly. This is done by comparing the spatially integrated movement detector response with the functional properties of the directionally-selective motion-sensitive. Horizontal Cells of the third visual ganglion of the fly's brain.These integrate local motion information over large parts of the visual field. The time course of the spatially integrated movement detector response is about proportional to the velocity of the stimulus pattern only as long as the pattern velocity and its time derivatives are sufficiently small. For large velocities and velocity changes of the stimulus pattern characteristic deviations of the response profiles from being proportional to pattern velocity are predicted on the basis of the detector theory developed here. These deviations are clearly reflected in the response of the wide-field Horizontal Cells, thus, providing very specific evidence that the movement detector theory developed here can be applied to motion detection in the fly. The characteristic dynamic features of the theoretically predicted and the experimentally determined cellular responses are exploited to estimate the time constant of the movement detector filter.     

Visual information processing in the fly's landing system

Summary When approaching a landing site flies (Musca domestica) extend their legs in order to prevent crash-landing. Pattern expansion in front of a tethered fly can mimic an approach towards a landing site. Under these conditions landing is a rather stereotyped motor pattern. Only the latency of the onset of the landing response varies with the stimulus strength. Quantitative studies of the stimulus-latency relationship led to the formulation of a simple model which describes the way movement information at the fly's retina is processed in order to trigger landing. We propose that the output of local directionally selective movement detectors are spatially pooled and subsequently integrated in time. Whenever the level of this integrated signal reaches a fixed threshold landing is released.     

Pattern-dependent response modulations in motion-sensitive visual interneurons--a model study

Even if a stimulus pattern moves at a constant velocity across the receptive field of motion-sensitive neurons, such as lobula plate tangential cells (LPTCs) of flies, the response amplitude modulates over time. The amplitude of these response modulations is related to local pattern properties of the moving retinal image. On the one hand, pattern-dependent response modulations have previously been interpreted as 'pattern-noise', because they deteriorate the neuron's ability to provide unambiguous velocity information. On the other hand, these modulations might also provide the system with valuable information about the textural properties of the environment. We analyzed the influence of the size and shape of receptive fields by simulations of four versions of LPTC models consisting of arrays of elementary motion detectors of the correlation type (EMDs). These models have previously been suggested to account for many aspects of LPTC response properties. Pattern-dependent response modulations decrease with an increasing number of EMDs included in the receptive field of the LPTC models, since spatial changes within the visual field are smoothed out by the summation of spatially displaced EMD responses. This effect depends on the shape of the receptive field, being the more pronounced--for a given total size--the more elongated the receptive field is along the direction of motion. Large elongated receptive fields improve the quality of velocity signals. However, if motion signals need to be localized the velocity coding is only poor but the signal provides--potentially useful--local pattern information. These modelling results suggest that motion vision by correlation type movement detectors is subject to uncertainty: you cannot obtain both an unambiguous and a localized velocity signal from the output of a single cell. Hence, the size and shape of receptive fields of motion sensitive neurons should be matched to their potential computational task.     

A two dimensional field theory for motion computation

The local extraction of motion information from brightness patterns by individual movement detectors of the correlation-type is considered in the first part of the paper. A two-dimensional field theory of movement detection is developed by treating the distance between two adjacent photoreceptors as a differential. In the first approximation of the theory we only consider linear terms of the time interval between the reception of a contrast element and its delayed representation by the detector and linear terms of the spatial distances between adjacent photoreceptors. As a result we may neglect terms of higher order than quadratic in a Taylor series development of the brightness pattern. The responses of pairs of individual movement detectors are combined to a local response vector. In the first approximation of the detector field theory the response vector is proportional to the instantaneous pattern velocity vector and linearly dependent on local properties of the moving pattern. The linear dependence on pattern properties is represented by a two by two tensor consisting of elements which are nonlinear, local functional of the moving pattern. Some of the properties of the tensor elements are treated in detail. So, for instance, it is shown that the off-diagonal elements of the tensor disappear when the moving pattern consists of x- and y-dependent separable components. In the second part of the paper the tensor relation leading to the output of a movement detector pair is spatially integrated. The result of the integration is an approximation to a summation of the outputs of an array of detector pairs. The spatially integrated detector tensor relates the translatory motion vector to the resultant output vector. It is shown that the angle between the motion vector and the resultant output vector is always smaller than ±90° whereas the angle between the motion vector and local response vectors, elicited by detector pairs, may cover the entire angular range. In the discussion of the paper the limits of the field theory for motion computation as well as its higher approximations are pointed out in some detail. In a special chapter the dependence of the detector response on the pattern properties is treated and in another chapter questions connected with the so called aperture problem are discussed. Furthermore, properties for compensation of the pattern dependent deviation angle by spatial physiological integration are mentioned in the discussion.     

Comparison between the movement detection systems underlying the optomotor and the landing response in the housefly

Flies evaluate movement within their visual field in order to control the course of flight and to elicit landing manoeuvres. Although the motor output of the two types of responses is quite different, both systems can be compared with respect to the underlying movement detection systems. For a quantitative comparison, both responses were measured during tethered flight under identical conditions. The stimulus was a sinusoidal periodic pattern of vertical stripes presented bilaterally in the fronto-lateral eye region of the fly. To release the landing response, the pattern was moved on either side from front to back. The latency of the response depends on the stimulus conditions and was measured by means of an infrared light-beam that was interrupted whenever the fly lifted its forelegs to assume a preprogrammed landing posture (Borst and Bahde 1986). As an optomotor stimulus the pattern moved on one side from front to back and on the other side in the opposite direction. The induced turning tendency was measured by a torque meter (Götz 1964). The response values which will be compared are the inverse latencies of the landing response and the amplitude of the yaw torque.

1. Optomotor course-control is more sensitive to pattern movement at small spatial wavelengths (10° and 20°) than the landing response (Fig. 1a and b). This suggests that elementary movement detectors (EMDs, Buchner 1976) with large detection base (the distance between interacting visual elements) contribute more strongly to the landing than to the optomotor system.
2. The optimum contrast frequencies of the different responses obtained at a comparatively high pattern contrast of about 0.6 was found to be between 1 and 10 Hz for the optomotor response, and around 20 Hz for the landing response (Fig. 2a and b). This discrepancy can be explained by the fact that the optomotor response was tested under stationary conditions (several seconds of stimulation) while for the landing response transient response characteristics of the movement detectors have to be taken into account (landing occurs under these conditions within less than 100 ms after onset of the movement stimulus). To test the landing system under more stationary conditions, the pattern contrast had to be reduced to low values. This led to latencies of several seconds. Then the optimum of the landing response is around 4 Hz. This is in the optimum range of the optomotor course-control response. The result suggests the same filter time constants for the movement detectors of both systems.
3. The dependence of both responses on the position and the size of the pattern was examined. The landing response has its optimum sensitivity more ventrally than the optomotor response (Fig. 3a and b). Both response amplitudes increase with the size of the pattern in a similar progression (Fig. 3c and d).

In first approximation, the present results are compatible with the assumption of a common set of movement detectors for both the optomotor course-control and the landing system. Movement detectors with different sampling bases and at different positions in the visual field seem to contribute with different gain to both responses. Accordingly, the control systems underlying both behaviors are likely to be independent already at the level of spatial integration of the detector output.     

Propagation of photon noise and information transfer in visual motion detection

The extraction of the direction of motion from the time varying retinal images is one of the most basic tasks any visual system is confronted with. However, retinal images are severely corrupted by photon noise, in particular at low light levels, thus limiting the performance of motion detection mechanisms of what sort so ever. Here, we study how photon noise propagates through an array of Reichardt-type motion detectors that are commonly believed to underlie fly motion vision. We provide closed-form analytical expressions of the signal and noise spectra at the output of such a motion detector array. We find that Reichardt detectors reveal favorable noise suppression in the frequency range where most of the signal power resides. Most notably, due to inherent adaptive properties, the transmitted information about stimulus velocity remains nearly constant over a large range of velocity entropies. Action editor: Matthew Wiener     

Motion-based analysis of spatial patterns by the human visual system

BACKGROUND: It is known that the visibility of patterns presented through stationary multiple slits is significantly improved by pattern movements. This study investigated whether this spatiotemporal pattern interpolation is supported by motion mechanisms, as opposed to the general belief that the human visual cortex initially analyses spatial patterns independent of their movements. RESULTS: Psychophysical experiments showed that multislit viewing could not be ascribed to such motion-irrelevant factors as retinal painting by tracking eye movements or an increase in the number of views by pattern movements. Pattern perception was more strongly impaired by the masking noise moving in the same direction than by the noise moving in the opposite direction, which indicates the direction selectivity of the pattern interpolation mechanism. A direction-selective impairment of pattern perception by motion adaptation also indicates the direction selectivity of the interpolation mechanism. Finally, the map of effective spatial frequencies, estimated by a reverse-correlation technique, indicates observers' perception of higher spatial frequencies, the recovery of which is theoretically impossible without the aid of motion information. CONCLUSIONS: These results provide clear evidence against the notion of separate analysis of pattern and motion. The visual system uses motion mechanisms to integrate spatial pattern information along the trajectory of pattern movement in order to obtain clear perception of moving patterns. The pattern integration mechanism is likely to be direction-selective filtering by V1 simple cells, but the integration of the local pattern information into a global figure should be guided by a higher-order motion mechanism such as MT pattern cells.     

A general model for visual motion detection

We propose a general model for detection of both first-order motion and second-order motion. In this model an input stimulus is divided into a number of partially overlapping spatiotemporal local regions. Spatiotemporal frequency analysis is done for every local region using Gabor filters, then the input stimulus (original spatiotemporal signal) is replaced by the outputs of Gabor filters. Local motion is detected by applying Gabor motion detectors to each local spatiotemporal pattern depicted by each local feature value. Outputs of all the detectors are integrated to give the final output for global motion of the input stimulus. The model was simulated on a computer and was confirmed to correctly detect second-order motion as well as first-order motion.     

Processing of figure and background motion in the visual system of the fly

The visual system of the fly is able to extract different types of global retinal motion patterns as may be induced on the eyes during different flight maneuvers and to use this information to control visual orientation. The mechanisms underlying these tasks were analyzed by a combination of quantitative behavioral experiments on tethered flying flies (Musca domestica) and model simulations using different conditions of oscillatory large-field motion and relative motion of different segments of the stimulus pattern. Only torque responses about the vertical axis of the animal were determined. The stimulus patterns consisted of random dot textures (Julesz patterns) which could be moved either horizontally or vertically. Horizontal rotatory large-field motion leads to compensatory optomotor turning responses, which under natural conditions would tend to stabilize the retinal image. The response amplitude depends on the oscillation frequency: It is much larger at low oscillation frequencies than at high ones. When an object and its background move relative to each other, the object may, in principle, be discriminated and then induce turning responses of the fly towards the object. However, whether the object is distinguished by the fly depends not only on the phase relationship between object and background motion but also on the oscillation frequency. At all phase relations tested, the object is detected only at high oscillation frequencies. For the patterns used here, the turning responses are only affected by motion along the horizontal axis of the eye. No influences caused by vertical motion could be detected. The experimental data can be explained best by assuming two parallel control systems with different temporal and spatial integration properties: TheLF-system which is most sensitive to coherent rotatory large-field motion and mediates compensatory optomotor responses mainly at low oscillation frequencies. In contrast, theSF-system is tuned to small-field and relative motion and thus specialized to discriminate a moving object from its background; it mediates turning responses towards objects mainly at high oscillation frequencies. The principal organization of the neural networks underlying these control systems could be derived from the characteristic features of the responses to the different stimulus conditions. The input to the model circuits responsible for the characteristic sensitivity of the SF-system to small-field and relative motion is provided by retinotopic arrays of local movement detectors. The movement detectors are integrated by a large-field element, the output cell of the network. The synapses between the detectors and the output cells have nonlinear transmission characteristics. Another type of large-field elements (pool cells) which respond to motion in front of both eyes and have characteristic direction selectivities are assumed to interact with the local movement detector channels by inhibitory synapses of the shunting type, before the movement detectors are integrated by the output cells. The properties of the LF-system can be accounted for by similar model circuits which, however, differ with respect to the transmission characteristic of the synapses between the movement detectors and the output cell; moreover, their pool cells are only monocular. This type of network, however, is not necessary to account for the functional properties of the LF-system. Instead, intrinsic properties of single neurons may be sufficient. Computer simulations of the postulated mechanisms of the SF-and LF-system reveal that these can account for the specific features of the behavioral responses under quite different conditions of coherent large-field motion and relative motion of different pattern segments.     

Mechanisms of dendritic integration underlying gain control in fly motion-sensitive interneurons

In the compensatory optomotor response of the fly the interesting phenomenon of gain control has been observed by Reichardt and colleagues (Reichardt et al., 1983): The amplitude of the response tends to saturate with increasing stimulus size, but different saturation plateaus are assumed with different velocities at which the stimulus is moving. This characteristic can already be found in the motion-sensitive large field neurons of the fly optic lobes that play a role in mediating this behavioral response (Hausen, 1982; Reichardt et al, 1983; Egelhaaf, 1985; Haag et al., 1992). To account for gain control a model was proposed involving shunting inhibition of these cells by another cell, the so-called pool cell (Reichardt et al., 1983), both cells sharing common input from an array of local motion detectors. This article describes an alternative model which only requires dendritic integration of the output signals of two types of local motion detectors with opposite polarity. The explanation of gain control relies on recent findings that these input elements are not perfectly directionally selective and that their direction selectivity is a function of pattern velocity. As a consequence, the resulting postsynaptic potential in the dendrite of the integrating cell saturates with increasing pattern size at a level between the excitatory and inhibitory reversal potentials. The exact value of saturation is then set by the activation ratio of excitatory and inhibitory input elements which in turn is a function of other stimulus parameters such as pattern velocity. Thus, the apparently complex phenomenon of gain control can be simply explained by the biophysics of dendritic integration in conjunction with the properties of the motion-sensitive input elements.     

Looking at Op Art from a computational viewpoint

Arts history tells an exciting story about repeated attempts to represent features that are crucial for the understanding of our environment and which, at the same time, go beyond the inherently two-dimensional nature of a flat painting surface: depth and motion. In the twentieth century, Op artists such as Bridget Riley began to experiment with simple black and white patterns that do not represent motion in an artistic way but actually create vivid dynamic illusions in static pictures. The cause of motion illusions in such paintings is still a matter of debate. The role of involuntary eye movements in this phenomenon is studied here with a computational approach. The possible consequences of shifting the retinal image of synthetic wave gratings, dubbed as 'riloids', were analysed by a two-dimensional array of motion detectors (2DMD model), which generates response maps representing the spatial distribution of motion signals generated by such a stimulus. For a two-frame sequence reflecting a saccadic displacement, these motion signal maps contain extended patches in which local directions change only little. These directions, however, do not usually precisely correspond to the direction of pattern displacement that can be expected from the geometry of the curved gratings as an instance of the so-called 'aperture problem'. The patchy structure of the simulated motion detector response to the displacement of riloids resembles the motion illusion, which is not perceived as a coherent shift of the whole pattern but as a wobbling and jazzing of ill-defined regions. Although other explanations are not excluded, this might support the view that the puzzle of Op Art motion illusions could potentially have an almost trivial solution in terms of small involuntary eye movement leading to image shifts that are picked up by well-known motion detectors in the early visual system. This view can have further consequences for our understanding of how the human visual system usually compensates for eye movements, in order to let us perceive a stable world despite continuous image shifts generated by gaze instability.     

Intensity and motion responses of giant vertical neurons of the fly eye

There are nine “giant vertical” neurons in the lobula plate of the fly optic lobe. Intracellular recordings were obtained from the three most peripheral of these cells. These cells respond to a light flash with graded changes in the membrane potential. The response consists of an “on” transient, a sustained depolarization, an increase in membrane potential fluctuations, and an “off” transient. Signal averaging showed that only the “on” and “off” transients are correlated to the stimulus. A pattern of horizontally oriented stripes moving in the vertical direction evokes a response larger than the response to a stationary pattern. The response is most sensitive to vertical movement; motion in the downward direction evokes a net membrane potential depolarization, and upward motion results in a net hyperpolarization. We conclude that the giant vertical cells function primarily as vertical motion detectors and that the direction of the motion is encoded in the polarity of the shift in the membrane potential.     

Orientation sensitive properties of visually driven neurons in extrastriate area 21a of cat cortex

Orientation sensitive properties of extrastriate area 21a neurons were investigated. Special attention was paid to the qualitative characteristics of neuron responses to the different orientations of visual stimulus motion across neuron classical receptive fields (CRF). The results of experiments have shown that a group of neurons (31%) in area 21a with specialized responses to moving visual stimuli changed their direction selective (DS) characteristics depending on the orientation of the stimulus movement. Some neurons reveal an abrupt drop of the direction sensitivity index (DI) to certain orientation (58%), and some show significant increase of DI at one of applied orientations of stimulus motion (22%). Detailed investigation of response patterns of non-directional neurons to different orientations of stimulus motion have revealed clear-cut qualitative differences, such as different regularities in the distribution of inter-peak inhibitory intervals in the response pattern in dependence of the orientation of stimulus motion. The investigation of neuron CRF stationary functional organization did not reveal correlations between RF's spatial functional organization, and that of qualitative modulations of neuron response patterns. A suggestion was put forward, that visual information central processing of orientation discrimination is a complex integrative process that includes quantitative as well as qualitative transformations of neuron activity.     

The Intrinsic Electrophysiological Characteristics of Fly Lobula Plate Tangential Cells: III. Visual Response Properties

In this last paper in a series (Borst and Haag, 1996; Haag et al., 1997) about the lobula plate tangential cells of the fly visual system (CH, HS, and VS cells), the visual response properties were examined using intracellular recordings and computer simulations. In response to visual motion stimuli, all cells responded mainly by a graded shift of their axonal membrane potential. While ipsilateral motion resulted in a graded membrane potential shift, contralateral motion led to distinct EPSPs. For HS cells, simultaneous extracellular recorded action potentials of a spiking interneuron, presumably the H2 cell, corresponded to the EPSPs in the HS cell in a one-to-one fashion. When HS cells were hyperpolarized during ipsilateral motion, they mainly produced action potentials, but when they were hyperpolarized during contralateral motion only a slight increase of EPSP amplitude, could be observed. Intracellular application of the sodium channel blocker QX 314 abolished action potentials of HS cells while having little effect on the graded membrane response to ipsilateral motion. HS and CH cells were also studied with respect to their spatial integration properties. For both cell types, their graded membrane response was found to increase less than linearly with the size of the ipsilateral motion pattern. However, while for HS cells various amounts of hyperpolarizing current injected during motion stimulation led to different saturation levels, this was not the case for CH cells. In response to a sinusoidal velocity modulation, CH cells followed pattern motion only up to 10 Hz modulation frequency, but HS cells still revealed significant membrane depolarizations up to about 40 Hz.In the computer simulations, the compartmental models of tangential cells, as derived in the previous papers, were linked to an array of local motion detectors. The model cells revealed the same basic response features as their natural counterparts. They showed a response saturation as a function of stimulus size. In CH-models, however, the saturation was less pronounced than in real CH-cells, indicating spatially nonuniform membrane resistances with higher values in the dendrite. As in the experiments, HS models responded to high-frequency velocity modulation with a higher amplitude than did CH models.     

用红外方法对蝇翅视动行为的探索

本文报告了利用红外装置对蝇翅视动行为实验研究的初步结果及其分析:1.在红外探测器探测到的信号中找到了一个能反映蝇翅拍动幅度的参数.2.双侧、单侧刺激域的宽度及刺激域的高度对视动反应发生几率在一定范围内正相关,当超过一阈值(即饱和阈值)后,即出现稳定的视动反应,它们的饱和阈值分别为60°,30°,40°刺激条纹的亮度生有类似情况.刺激条纹的运动速度在一定范围内对视动反应无影响.3.当刺激没有达到饱和时,蝇翅出现断续的典型的视动反应,即“0-1波动反应”.4.单侧条纹由前向后运动时,蝇翅出现典型反应,而条纹从后向前运动时,不出现典型的视动反应或反应很弱.双侧刺激时,条纹向前运动几乎不诱发反应;条纹向后运动诱发明显的蝇翅视动反应,且蝇翅平面的方向在拍动过程中发生变化.     

Subcellular mapping of dendritic activity in optic flow processing neurons

Dendritic integration is a fundamental element of neuronal information processing. So far, few studies have provided a detailed spatial picture of this process, describing the properties of local dendritic activity and its subcellular organization. Here, we used 2-photon calcium imaging in optic flow processing neurons of the fly Calliphora vicina to determine the preferred location and direction of local motion cues for small branchlets throughout the entire dendrite. We found a pronounced retinotopic mapping on both the subcellular and the cell population level. In addition, dendritic branchlets residing in different layers of the neuropil were tuned to distinct directions of motion. Summing the local receptive fields of all dendritic branchlets reproduced the characteristic properties of these neurons’ axonal output receptive fields. Our results corroborate the notion that the dendritic morphology of vertical system cells allows them to selectively collect local motion inputs with particular directional preferences from a spatially organized input repertoire, thus forming filters that match global patterns of optic flow. Furthermore, we suggest that the facet arrangement across the fly’s eye shapes the subcellular direction tuning to local motion stimuli. These data illustrate a highly structured circuit organization as an efficient way to hard-wire a complex sensory task.     

基于EMDs阵列和BP网络的视觉运动感知过程

以Ｒｅｉｃｈａｒｄｔ的相关型初级运动检测器阵列和Ｒｕｍｅｌｈａｒｔ的误差反传学习（ｌｅａｒｎｉｎｇｂｙｂａｃｋ－ｐｒｏｐａｇａｔｉｎｇｅｒｒｏｒｓ,ＢＰ）网络相结合构成了一个视觉运动感知神经网络,探讨了视觉运动信息的感知过程。试图从计算神经科学的观点来阐明从一推运动分量的检测到二维模式运动感知的神经原理,从而回答运动矢量在脑内如何表征。计算机仿真表明,在有监督学习的条件下,网络可以学会解决局城运动检测所带来的多义性问题,给出模式的真实朝向、运动方向和运动速度。     

Orientation sensitivity of the visual movement detection system activating the landing response of the blowflies,Calliphora, and Phaenicia: A behavioural investigation

The orientation sensitivity of the visual movement detection system relative to the axes of the eye was investigated for the landing response by changing the direction of movement of a periodic striped pattern (unidirectional movement) and a two-stripe pattern consiting of two stripes moving apart (bidirectional movement). In the momocular, equatorial regions of the eye progressive motion proves to be most effective, whereas in the frontal (equational), binocular region descendive motion is most effective in eliciting the landing response, probably caused by binocular interactions. A strong enhancement of the response is induced by stimulation in the binocular region of the two eyes. The orientation of elementary movement detectors relative to the axes of the ommatidial array is discussed. The findings are summarized in a functional model of the landing response.     

Ratios of template responses as the basis of semivision.

The template model starts with a layer of receptors that in the case of vision are leaky detectors or counters of photons. In many animals, the ratio of the responses of a few spectral types is the basis of colour vision irrespective of intensity. Ratios of template responses are now introduced as the basis of form discrimination. In insects, the second-order neurons on the visual pathway appear to detect temporal contrast at the spatial resolution of the retina. At the next level, in the optic medulla, we find a large number of small local neurons in a column on each visual axis. The template theory is a hypothesis about how the above system functions. All possible combinations of positive, indeterminate or negative temporal contrast are considered, at two adjacent visual axes at two successive instants, giving 81 possible local templates. These templates are therefore phasic detectors of all the possible spatiotemporal contrast combinations. Some of the template responses indicate polarity of edge, flicker, or direction of motion and other abstracted features of the stimulus pattern with the maximum spatial and temporal resolution. The ratios of numbers of template responses, in higher fields at a higher level, yield quantitative measures of the qualities of edges independently of the number of edges, but taking ratios causes a corresponding loss of the spatiotemporal resolution and the pattern within each field. Templates respond to transients without computation, are readily modified or selected in evolution and can be simulated in artificial vision.