Neural network architectures for motion perception and elementary motion detection in the fly visual system

Dragonfly visual evolutionary neural network: A novel bionic optimizer with related LSGO and engineering design optimization

Biological visual systems intrinsically include multiple kinds of motion-sensitive neurons. Some of them have been successfully used to construct neural computational models for problem-specific engineering applications such as motion detection, object tracking, etc. Nevertheless, it remains unclear how these neurons' response mechanisms can be contributed to the topic of optimization. Hereby, the dragonfly's visual response mechanism is integrated with the inspiration of swarm evolution to develop a dragonfly visual evolutionary neural network for large-scale global optimization (LSGO) problems. Therein, a grayscale image input-based dragonfly visual neural network online outputs multiple global learning rates, and later, such learning rates guide a population evolution-like state update strategy to seek the global optimum. The comparative experiments show that the neural network is a competitive optimizer capable of effectively solving LSGO benchmark suites with 2000 dimensions per example and the design of an operational amplifier.

Effects of spatial attention on spatial and temporal acuity: A computational account

In our daily lives, the visual system receives a plethora of visual information that competes for the brain's limited processing capacity. Nevertheless, not all visual information is useful for our cognitive, emotional, social, and ultimately survival purposes. Therefore, the brain employs mechanisms to select critical information and thereby optimizes its limited resources. Attention is the selective process that serves such a function. In particular, covert spatial attention - attending to a particular location in the visual field without eye movements - improves spatial resolution and paradoxically deteriorates temporal resolution. The neural correlates underlying these attentional effects still remainelusive. In this work, we tested a neural model's predictions that explain these phenomena based on interactions between channels with different spatiotemporal sensitivities - namely, the magnocellular (transient) and parvocellular (sustained) channels. More specifically, our model postulates that spatial attention enhances activities in the parvocellular pathway, thereby producing improved performance in spatial resolution tasks. However, the enhancement of parvocellular activities leads to decreased magnocellular activities due to parvo-magno inhibitory interactions. As a result, spatial attention hampers temporal resolution. We compared the predictions of the model to psychophysical data, and show that our model can account qualitatively and quantitatively for the effects of spatial attention on spatial and temporal acuity.

Complete Coverage Path Planning of an Unmanned Surface Vehicle Based on a Complete Coverage Neural Network Algorithm

In practical applications, an unmanned surface vehicle (USV) generally employs a task of complete coverage path planning for exploration in a target area of interest. The biological inspired neural network (BINN) algorithm has been extensively employed in path planning of mobile robots, recently. In this paper, a complete coverage neural network (CCNN) algorithm for the path planning of a USV is proposed for the first time. By simplifying the calculation process of the neural activity, the CCNN algorithm can significantly reduce calculation time. To improve coverage efficiency and make the path more regular, the optimal next position decision formula combined with the covering direction term is established. The CCNN algorithm has increased moving directions of the path in grid maps, which in turn has further reduced turning-angles and makes the path smoother. Besides, an improved A* algorithm that can effectively decrease path turns is presented to escape the deadlock. Simulations are carried out in different environments in this work. The results show that the coverage path generated by the CCNN algorithm has less turning-angle accumulation, deadlocks, and calculation time. In addition, the CCNN algorithm is capable to maintain the covering direction and adapt to complex environments, while effectively escapes deadlocks. It is applicable for USVs to perform multiple engineering missions.

Trajectory planning and tracking control of a ground mobile robot:A reconstruction approach towards space vehicle

With the development of the similarity calculation method, the orbital motion of space vehicle can be translated into a sequence of waypoints that reflect position and velocity on the ground. In this paper, a motion control system is proposed to make the mobile robot pass through the desired waypoints for reconstructing the orbital motion. First, the parameterized trajectory optimization method is applied to generate a curvature-continuous trajectory from the waypoints, the position and velocity demands are presented as the equality constraints. Virtual positions are introduced to reduce the oscillation, and the total execution time of the whole trajectory is selected as the optimization parameter to reduce the computational burden. Then, an equivalence transformation is provided to translate the error system into an affine form, which is beneficial for the feedback controller design. Based on this, a nonlinear trajectory tracking controller is proposed, which includes a feedforward controller and an error feedback controller, and its exponential stability is proved using Persistency of Excitation Lemma. In addition, a shunting neural dynamics model is employed to avoid sharp velocity jumps. Finally, the performed experiments verify the effectiveness of the proposed method.

Multi-AUV Target Search Based on Bioinspired Neurodynamics Model in 3-D Underwater Environments

Target search in 3-D underwater environments is a challenge in multiple autonomous underwater vehicles (multi-AUVs) exploration. This paper focuses on an effective strategy for multi-AUV target search in the 3-D underwater environments with obstacles. First, the Dempster-Shafer theory of evidence is applied to extract information of environment from the sonar data to build a grid map of the underwater environments. Second, a topologically organized bioinspired neurodynamics model based on the grid map is constructed to represent the dynamic environment. The target globally attracts the AUVs through the dynamic neural activity landscape of the model, while the obstacles locally push the AUVs away to avoid collision. Finally, the AUVs plan their search path to the targets autonomously by a steepest gradient descent rule. The proposed algorithm deals with various situations, such as static targets search, dynamic targets search, and one or several AUVs break down in the 3-D underwater environments with obstacles. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve search task of multiple targets with higher efficiency and adaptability compared with other algorithms.

The Path Planning of AUV Based on D-S Information Fusion Map Building and Bio-Inspired Neural Network in Unknown Dynamic Environment

In this paper a biologically inspired neural dynamics and map planning based approach are simultaneously proposed for AUV (Autonomous Underwater Vehicle) path planning and obstacle avoidance in an unknown dynamic environment. Firstly the readings of an ultrasonic sensor are fused into the map using the D-S (Dempster-Shafer) inference rule and a two-dimensional occupancy grid map is built. Secondly the dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation. The AUV path is autonomously generated from the dynamic activity landscape of the neural network and previous AUV location. Finally, simulation results show high quality path optimization and obstacle avoidance behaviour for the AUV.

Neural network approaches to dynamic collision-free trajectory generation

In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons. The real-time optimal trajectory is generated through the dynamic activity landscape of the neural network without explicitly searching over the free space nor the collision paths, without explicitly optimizing any global cost functions, without any prior knowledge of the dynamic environment, and without any learning procedures. Therefore the model algorithm is computationally efficient. The stability of the neural network system is guaranteed by the existence of a Lyapunov function candidate. In addition, this model is not very sensitive to the model parameters. Several model variations are presented and the differences are discussed. As examples, the proposed models are applied to generate collision-free trajectories for a mobile robot to solve a maze-type of problem, to avoid concave U-shaped obstacles, to track a moving target and at the same to avoid varying obstacles, and to generate a trajectory for a two-link planar robot with two targets. The effectiveness and efficiency of the proposed approaches are demonstrated through simulation and comparison studies.

Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments

Multiple robot cooperation is a challenging and critical issue in robotics. To conduct the cooperative hunting by multirobots in unknown and dynamic environments, the robots not only need to take into account basic problems (such as searching, path planning, and collision avoidance), but also need to cooperate in order to pursue and catch the evaders efficiently. In this paper, a novel approach based on a bioinspired neural network is proposed for the real-time cooperative hunting by multirobots, where the locations of evaders and the environment are unknown and changing. The bioinspired neural network is used for cooperative pursuing by the multirobot team. Some other algorithms are used to enable the robots to catch the evaders efficiently, such as the dynamic alliance and formation construction algorithm. In the proposed approach, the pursuing alliances can dynamically change and the robot motion can be adjusted in real-time to pursue the evader cooperatively, to guarantee that all the evaders can be caught efficiently. The proposed approach can deal with various situations such as when some robots break down, the environment has different boundary shapes, or the obstacles are linked with different shapes. The simulation results show that the proposed approach is capable of guiding the robots to achieve the hunting of multiple evaders in real-time efficiently.

How does binocular rivalry emerge from cortical mechanisms of 3-D vision?

Under natural viewing conditions, a single depthful percept of the world is consciously seen. When dissimilar images are presented to corresponding regions of the two eyes, binocular rivalry may occur, during which the brain consciously perceives alternating percepts through time. How do the same brain mechanisms that generate a single depthful percept of the world also cause perceptual bistability, notably binocular rivalry? What properties of brain representations correspond to consciously seen percepts? A laminar cortical model of how cortical areas V1, V2, and V4 generate depthful percepts is developed to explain and quantitatively simulate binocular rivalry data. The model proposes how mechanisms of cortical development, perceptual grouping, and figure-ground perception lead to single and rivalrous percepts. Quantitative model simulations of perceptual grouping circuits demonstrate influences of contrast changes that are synchronized with switches in the dominant eye percept, gamma distribution of dominant phase durations, piecemeal percepts, and coexistence of eye-based and stimulus-based rivalry. The model as a whole also qualitatively explains data about the involvement of multiple brain regions in rivalry, the effects of object attention on switching between superimposed transparent surfaces, monocular rivalry, Marroquin patterns, the spread of suppression during binocular rivalry, binocular summation, fusion of dichoptically presented orthogonal gratings, general suppression during binocular rivalry, and pattern rivalry. These data explanations follow from model brain mechanisms that assure non-rivalrous conscious percepts.

Musca domestica inspired machine vision sensor with hyperacuity

A fiber optic sensor inspired by the compound eye of the common housefly, Musca domestica, has been developed. The sensor coupled with analog preprocessing hardware has the potential to extract edge information quickly and in parallel. The design is motivated by the parallel nature of the fly's vision system and its demonstrated hyperacuity or precision of visual localization beyond the conventional resolution limit. The fly's anatomy supporting the design is reviewed, followed by the design of a one-dimensional, cartridge-based sensor. The sensor's ability to locate a line stimulus in a two-dimensional space is demonstrated. Discussion is provided to extend this work in scale, cartridge dimension, information and array processing.

Real-time collision-free motion planning of a mobile robot using a Neural Dynamics-based approach

A neural dynamics based approach is proposed for real-time motion planning with obstacle avoidance of a mobile robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation. The real-time collision-free robot motion is planned through the dynamic neural activity landscape of the neural network without any learning procedures and without any local collision-checking procedures at each step of the robot movement. Therefore the model algorithm is computationally simple. There are only local connections among neurons. The computational complexity linearly depends on the neural network size. The stability of the proposed neural network system is proved by qualitative analysis and a Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.

Attraction of flashes to moving dots

Motion is known to distort visual space, producing illusory mislocalizations for flashed objects. Previously, it has been shown that when a stationary bar is flashed in the proximity of a moving stimulus, the position of the flashed bar appears to be shifted in the direction of nearby motion. A model consisting of predictive projections from the sub-system that processes motion information onto the sub-system that processes position information can explain this illusory position shift of a stationary flashed bar in the direction of motion. Based on this model of motion-position interactions, we predict that the perceived position of a flashed stimulus should also be attracted towards a nearby moving stimulus. In the first experiment, observers judged the perceived vertical position of a flash with respect to two horizontally moving dots of unequal contrast. The results of this experiment were in agreement with our prediction of attraction towards the high contrast dot. We obtained similar findings when the moving dots were replaced by drifting gratings of unequal contrast. In control experiments, we found that neither attention nor eye movements can account for this illusion. We propose that the visual system uses predictive influences from the motion processing sub-system on the position processing sub-system to overcome the temporal limitations of the position processing system.

A neural model of how the brain represents and compares multi-digit numbers: spatial and categorical processes

Both animals and humans represent and compare numerical quantities, but only humans have evolved multi-digit place-value number systems. This article develops a Spatial Number Network, or SpaN, model to explain how these shared numerical capabilities are computed using a spatial representation of number quantities in the Where cortical processing stream, notably the inferior parietal cortex. Multi-digit numerical representations that obey a place-value principle are proposed to arise through learned interactions between categorical language representations in the What cortical processing stream and the Where spatial representation. Learned semantic categories that symbolize separate digits, as well as place markers like 'ty,' 'hundred,' and 'thousand,' are associated through learning with the corresponding spatial locations of the Where representation. Such What-to-Where auditory-to-visual learning generates place-value numbers as an emergent property, and may be compared with other examples of multi-modal cross-modality learning, including synesthesia. The model quantitatively simulates error rates in quantification and numerical comparison tasks, and reaction times for number priming and numerical assessment and comparison tasks. In the Where cortical process, transient responses to inputs are integrated before they activate an ordered spatial map that selectively responds to the number of events in a sequence and exhibits Weber law properties. Numerical comparison arises from activity pattern changes across the spatial map that define a 'directional comparison wave.' Variants of these model mechanisms have elsewhere been used to explain data about other Where stream phenomena, such as motion perception, spatial attention, and target tracking. The model is compared with other models of numerical representation.

The imbalanced brain: from normal behavior to schizophrenia

An outstanding problem in psychiatry concerns how to link discoveries about the pharmacological, neurophysiological, and neuroanatomical substrates of mental disorders to the abnormal behaviors that they control. A related problem concerns how to understand abnormal behaviors on a continuum with normal behaviors. During the past few decades, neural models have been developed of how normal cognitive and emotional processes learn from the environment, focus attention and act upon motivationally important events, and cope with unexpected events. When arousal or volitional signals in these models are suitably altered, they give rise to symptoms that strikingly resemble negative and positive symptoms of schizophrenia, including flat affect, impoverishment of will, attentional problems, loss of a theory of mind, thought derailment, hallucinations, and delusions. This article models how emotional centers of the brain, such as the amygdala, interact with sensory and prefrontal cortices (notably ventral, or orbital, prefrontal cortex) to generate affective states, attend to motivationally salient sensory events, and elicit motivated behaviors. Closing this feedback loop between cognitive and emotional centers is predicted to generate a cognitive-emotional resonance that can support conscious awareness. When such emotional centers become depressed, negative symptoms of schizophrenia emerge in the model. Such emotional centers are modeled as opponent affective processes, such as fear and relief, whose response amplitude and sensitivity are calibrated by an arousal level and chemical transmitters that slowly inactivate, or habituate, in an activity-dependent way. These opponent processes exhibit an Inverted-U, whereby behavior becomes depressed if the arousal level is chosen too large or too small. The negative symptoms are owing to the way in which the depressed opponent process interacts with other circuits throughout the brain.

Neural dynamics of binocular brightness perception

How does the visual cortex combine information from both eyes to generate perceptual representations of object surfaces? Important clues about this process may be derived from data about the perceived brightness of surface regions under binocular viewing conditions, including data about binocular brightness summation in response to Ganzfelds, the U-shaped data of Fechner's paradox that violates binocular brightness summation, and the effects of different combinations of monocular and binocular contours and surface luminance differences on threshold sensitivity to monocular flashes of light. How to reconcile these apparently contradictory data properties has been a severe challenge to previous models, and none has explained them all. The present article quantitatively simulates them all by further developing the FACADE vision model. Key model processes discount the illuminant and compute image contrasts in each monocular channel using shunting on-center off-surround networks; binocularly fuse these discounted monocular signals using shunting on-center off-surround networks with nonlinear excitatory and inhibitory signals; and use these binocularly fused activities to trigger filling-in of a binocular surface representation that represents perceived surface brightness. Previous models that have suggested explanations of subsets of these data are discussed.

Neural model of first-order and second-order motion perception and magnocellular dynamics

A neural model of motion perception simulates psychophysical data concerning first-order and second-order motion stimuli, including the reversal of perceived motion direction with distance from the stimulus (gamma display), and data about directional judgments as a function of relative spatial phase or spatial and temporal frequency. Many other second-order motion percepts that have ascribed to a second non-Fourier processing stream can also be explained in the model by interactions between ON and OFF cells within a single, neurobiologically interpreted magnocellular processing stream. Yet other percepts may be traced to interactions between form and motion processing streams, rather than to processing within multiple motion processing streams. The model hereby explains why monkeys with lesions of the parvocellular layers, but not of the magnocellular layers, of the lateral geniculate nucleus (LGN) are capable of detecting the correct direction of second-order motion, why most cells in area MT are sensitive to both first-order and second-order motion, and why after 2-amino-4-phosphonobutyrate injection selectively blocks retinal ON bipolar cells, cortical cells are sensitive only to the motion of a moving bright bar's trailing edge. Magnocellular LGN cells show relatively transient responses, whereas parvocellular LGN cells show relatively sustained responses. Correspondingly, the model bases its directional estimates on the outputs of model ON and OFF transient cells that are organized in opponent circuits wherein antagonistic rebounds occur in response to stimulus offset. Center-surround interactions convert these ON and OFF outputs into responses of lightening and darkening cells that are sensitive both to direct inputs and to rebound responses in their receptive field centers and surrounds. The total pattern of activity increments and decrements is used by subsequent processing stages (spatially short-range filters, competitive interactions, spatially long-range filters, and directional grouping cells) to determine the perceived direction of motion.

Motion deblurring in a neural network model of retino-cortical dynamics

Simulations of a neural network model of retino-cortical dynamics (Oğmen H, Neural Netw 6 (1993) 245-273) are presented. The temporal-step response of the model to a single dot (spatial impulse) consists of three post-retinal phases: reset, feed-forward dominant and feedback dominant. In response to a single moving dot, the model predicts the perception of extensive blur. This extensive blur is proposed to be due to the relative spatial and temporal offsets between transient and sustained signals conveyed from retina to post-retinal levels. In response to a pair of horizontally separated dots moving in the horizontal direction, the model predicts extensive blur for the trailing dot irrespective of dot-to-dot separation. For the leading dot, the model predicts a decrease in perceived blur for long exposure durations when dot-to-dot separations are small. The reduction of perceived blur at long exposure durations for small dot-to-dot separations is proposed to stem from the spatio-temporal overlap between the transient activity generated by the trailing dot and the sustained activity generated by the leading dot. The model also predicts that targets moving at higher speeds generate more blur even when blur is normalized with respect to speed. The mechanism in the model generating this effect is a slow inhibition within the sustained channel. These predictions are compared with recent psychophysical data (Chen S, Bedell HE, Oğmen H, Vis Res 35 (1995) 2315-2328) and are found to be in excellent agreement. The model is used to offer a coherent explanation for several controversial findings published in the literature. This computational study shows that a model without any motion-compensation mechanism can give a good account of motion deblurring phenomenon and supplements our recent experimental study which provided evidence against motion-compensation type models in explaining the motion deblurring phenomenon (Chen S, Bedell HE, Oğmen H, Vis Res 35 (1995) 2315-2328).

Cortical dynamics of form and motion integration: persistence, apparent motion, and illusory contours

How does the visual system generate percepts of moving forms? How does this happen when the forms are emergent percepts, such as illusory contours or segregated textures, and the motion percept is apparent motion between the emergent forms? We develop a neural model of form-motion interactions to explain and simulate parametric properties of psychophysical motion data and to make predictions about how the parallel cortical processing streams V1-->MT and V1-->V2-->MT control form-motion interactions. The model explains how an illusory contour can move in apparent motion to another illusory contour or to a luminance-derived contour; how illusory contour persistence relates to the upper interstimulus interval (ISI) threshold for apparent motion; and how upper and lower ISI thresholds for seeing apparent motion between two flashes decrease with stimulus duration and narrow with spatial separation (Korte's laws). The model accounts for these data by suggesting how the persistence of a boundary segmentation in the V1-->V2 processing stream influences the quality of apparent motion in the V1-->MT stream through V2-->MT interactions. These data may all be explained by an analysis of how orientationally tuned form perception mechanisms and directionally tuned motion perception mechanisms interact.

The effect of flicker on foveal and peripheral thresholds for oscillatory motion

This study evaluated the influence of superimposed luminance flicker on the detection of oscillatory motion. Thresholds for oscillatory motion were determined in the fovea and at 2, 6 and 25 deg in the right field for a small luminous target with and without sinusoidal luminance flicker. At the fovea, flicker modulation up to 80% at frequencies from 1.5 to 9 Hz had no effect on motion detection, except for oscillatory motion at a frequency of 8 Hz, for which thresholds were elevated by about 0.2 log units. In the periphery, flicker elevated motion thresholds up to 0.3-0.4 log units at low and moderate frequencies of oscillation at all locations tested. However, both foveal and peripheral motion thresholds were unaffected by flicker when the luminance of the target was reduced. The absence of a robust effect of target flicker on motion thresholds may be accounted for in part by the comparison of activity across a large population of motion-detecting neurons with different direction preferences. Another contributing factor may be the existence of foveal velocity- and position-detecting mechanisms with similar sensitivities.

Quantitative studies of fly visual sustained neurons

We present a quantitative study of a neural network model [1] proposed for the sustained neurons in the fly visual system. Electrophysiological recordings of sustained neurons [2] are digitized and transferred to a computer. A numerical ordinary differential equation solver is used to simulate the model. In order to obtain an initial set of parameters, we introduce approximations to the model and obtain fits to parts of the response characteristics. These initial parameter values are then refined by optimization routines. The model is compared to data in 4 different experimental paradigms and in general is in good agreement with data. We conclude that the simplified versions of temporal and spatial adaptation mechanisms of the model capture the essential features of the dynamics of sustained neurons and that the refinement of the model requires further experimental studies to elucidate the number of stages involved in temporal adaptation as well as the precise shape of the relationship between the membrane potentials and the spike frequency for the sustained neurons.

Simulations of X and Y retinal ganglion cell behavior with a nonlinear push-pull model of spatiotemporal retinal processing

This article describes a nonlinear model of neural processing in the vertebrate retina, comprising model photoreceptors, model push-pull bipolar cells, and model ganglion cells. Previous analyses and simulations have shown that with a choice of parameters that mimics beta cells, the model exhibits X-like linear spatial summation (null response to contrast-reversed gratings) in spite of photoreceptor nonlinearities; on the other hand, a choice of parameters that mimics alpha cells leads to Y-like frequency doubling. This article extends the previous work by showing that the model can replicate qualitatively many of the original findings on X and Y cells with a fixed choice of parameters. The results generally support the hypothesis that X and Y cells can be seen as functional variants of a single neural circuit. The model also suggests that both depolarizing and hyperpolarizing bipolar cells converge onto both ON and OFF ganglion cell types. The push-pull connectivity enables ganglion cells to remain sensitive to deviations about the mean output level of nonlinear photoreceptors. These and other properties of the push-pull model are discussed in the general context of retinal processing of spatiotemporal luminance patterns.

A neural model for nonassociative learning in a prototypical sensory-motor scheme: the landing reaction in flies

Nonassociative learning is an important property of neural organization in both vertebrate and invertebrate species. In this paper we propose a neural model for nonassociative learning in a well studied prototypical sensory-motor scheme: the landing reaction of flies. The general structure of the model consists of sensory processing stages, a sensory-motor gate network, and motor control circuits. The paper concentrates on the sensory-motor gate network which has an agonist-antagonist structure. Sensory inputs to this circuit are transduced by chemical messenger systems whose dynamics include depletion and replenishment terms. The resulting circuit is a gated dipole anatomy and we show that it gives a good account of nonassociative learning in the landing reaction of the fly.

Are there separate ON and OFF channels in fly motion vision?

Visual information is processed in a series of subsequent steps. The performance of each of these steps depends not only on the computations it performs itself but also on the representation of the visual surround on which it operates. Here we investigate the consequences of signal preprocessing for the performance of the motion-detection system of the fly. In particular, we analyze whether the retinal input signals are rectified and segregate into separate ON and OFF channels, which then feed independent parallel motion-detection pathways. We recorded the activity of an identified directionally selective interneuron (H1-cell) in response to apparent motion stimuli, i.e. sequential brightness changes at two neighboring locations in the visual field, as well as to brightness changes at only a single location. For apparent motion stimuli, the motion-dependent response component was determined by subtracting from the overall response to the individual stimulus components when presented alone. The following conclusions could be derived: (1) Apparent motion consisting of a sequence of increased or decreased brightness at two locations in the visual field have the same optimum interstimulus time interval (Fig. 3). (2) Sequences of brightness steps of like polarity (either increments or decrements) elicit positive and negative motion-dependent response components when mimicking motion in the cell's preferred and null direction, respectively. The motion-dependent response components are inverted in sign when the brightness steps of a stimulus sequence have a different polarity (Fig. 7). (3) The responses to the beginning and the end of a brightness pulse depend on the pulse duration. For pulse durations of less than 2 s, both events interact with each other (Fig. 9). All of these results do not provide any indication that the fly processes motion information in independent ON and OFF motion detectors. Brightness changes of both signs are rather represented at the input of the same movement detectors, and interactions between signals resulting from both brightness increments and decrements take their sign into account. This type of preprocessing of the retinal input is argued to render a motion-detection system particularly robust against noise.