A multi-differential neuromorphic approach to motion detection

This paper presents a multi-differential neuromorphic approach to motion detection. The model is based evidence for a differential operators interpretation of the properties of the cortical motion pathway. We discuss how this strategy, which provides a robust measure of speed for a range of types of image motion using a single computational mechanism, forms a useful framework in which to develop future neuromorphic motion systems. We also discuss both our approaches to developing computational motion models, and constraints in the design strategy for transferring motion models to other domains of early visual processing.     

Non-Conscious Processing of Motion Coherence Can Boost Conscious Access

Research on the scope and limits of non-conscious vision can advance our understanding of the functional and neural underpinnings of visual awareness. Here we investigated whether distributed local features can be bound, outside of awareness, into coherent patterns. We used continuous flash suppression (CFS) to create interocular suppression, and thus lack of awareness, for a moving dot stimulus that varied in terms of coherence with an overall pattern (radial flow). Our results demonstrate that for radial motion, coherence favors the detection of patterns of moving dots even under interocular suppression. Coherence caused dots to break through the masks more often: this indicates that the visual system was able to integrate low-level motion signals into a coherent pattern outside of visual awareness. In contrast, in an experiment using meaningful or scrambled biological motion we did not observe any increase in the sensitivity of detection for meaningful patterns. Overall, our results are in agreement with previous studies on face processing and with the hypothesis that certain features are spatiotemporally bound into coherent patterns even outside of attention or awareness.     

Defining the computational structure of the motion detector in Drosophila

Many animals rely on visual motion detection for survival. Motion information is extracted from spatiotemporal intensity patterns on the retina, a paradigmatic neural computation. A phenomenological model, the Hassenstein-Reichardt correlator (HRC), relates visual inputs to neural activity and behavioral responses to motion, but the circuits that implement this computation remain unknown. By using cell-type specific genetic silencing, minimal motion stimuli, and in?vivo calcium imaging, we examine two critical HRC inputs. These two pathways respond preferentially to light and dark moving edges. We demonstrate that these pathways perform overlapping but complementary subsets of the computations underlying the HRC. A numerical model implementing differential weighting of these operations displays the observed edge preferences. Intriguingly, these pathways are distinguished by their sensitivities to a stimulus correlation that corresponds to an illusory percept, "reverse phi," that affects many species. Thus, this computational architecture may be widely used to achieve edge selectivity in motion detection.     

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.     

Relating priming and repetition suppression

We present a prototype of a recently proposed two stage model of the entorhinal-hippocampal loop. Our aim is to form a general computational model of the sensory neocortex. The model--grounded on pure information theoretic principles--accounts for the most characteristic features of long-term memory (LTM), performs bottom-up novelty detection, and supports noise filtering. Noise filtering can also serve to correct the temporal ordering of information processing. Surprisingly, as we examine the temporal characteristics of the model, the emergent dynamics can be interpreted as perceptual priming, a fundamental type of implicit memory. In the model's framework, computational results support the hypothesis of a strong correlation between perceptual priming and repetition suppression and this correlation is a direct consequence of the temporal ordering in forming the LTM. We also argue that our prototype offers a relatively simple and coherent explanation of priming and its relation to a general model of information processing by the brain.     

A model for the detection of moving targets in visual clutter inspired by insect physiology

We present a computational model for target discrimination based on intracellular recordings from neurons in the fly visual system. Determining how insects detect and track small moving features, often against cluttered moving backgrounds, is an intriguing challenge, both from a physiological and a computational perspective. Previous research has characterized higher-order neurons within the fly brain, known as 'small target motion detectors' (STMD), that respond robustly to moving features, even when the velocity of the target is matched to the background (i.e. with no relative motion cues). We recorded from intermediate-order neurons in the fly visual system that are well suited as a component along the target detection pathway. This full-wave rectifying, transient cell (RTC) reveals independent adaptation to luminance changes of opposite signs (suggesting separate ON and OFF channels) and fast adaptive temporal mechanisms, similar to other cell types previously described. From this physiological data we have created a numerical model for target discrimination. This model includes nonlinear filtering based on the fly optics, the photoreceptors, the 1(st) order interneurons (Large Monopolar Cells), and the newly derived parameters for the RTC. We show that our RTC-based target detection model is well matched to properties described for the STMDs, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter' to successfully detect most targets from the background. Importantly, this model can explain this type of feature discrimination without the need for relative motion cues.     

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。     

A model of motion adaptation and motion after-effects based upon principal component regression

A computational model to help explain effects of adaptation to moving signals is compared with established energy (linear regression) models of motion detection. The proposed model assumes that processed image signals are subject to error in both dimensions of space and time. This assumption constrains models of motion perception to be based upon principal component regression rather than linear regression. It is shown that response suppression of model complex cell neurons that input into the model may account for (1) increases in perceived speed after adaptation to static patterns and testing with slowly moving patterns, (2) significant increases in perceived speed after adaptation to patterns moving at a medium speed and testing at high speed, and (3) decreases in perceived speed in the opponent direction to a quickly moving adapting signal. Neither of predictions (2) or (3) are general features of established accounts of motion detection by visual processes based upon linear regression. Comparisons of the proposed model's speed transfer function with existing psychophysical data suggests that the visual system processes motion signals with the tacit assumption that image measurements are subject to error in both space and time. Received: 24 January 2000 / Accepted in revised form: 8 May 2000     

Coherent Motion of Monolayer Sheets under Confinement and Its Pathological Implications

Coherent angular rotation of epithelial cells is thought to contribute to many vital physiological processes including tissue morphogenesis and glandular formation. However, factors regulating this motion, and the implications of this motion if perturbed, remain incompletely understood. In the current study, we address these questions using a cell-center based model in which cells are polarized, motile, and interact with the neighboring cells via harmonic forces. We demonstrate that, a simple evolution rule in which the polarization of any cell tends to orient with its velocity vector can induce coherent motion in geometrically confined environments. In addition to recapitulating coherent rotational motion observed in experiments, our results also show the presence of radial movements and tissue behavior that can vary between solid-like and fluid-like. We show that the pattern of coherent motion is dictated by the combination of different physical parameters including number density, cell motility, system size, bulk cell stiffness and stiffness of cell-cell adhesions. We further observe that perturbations in the form of cell division can induce a reversal in the direction of motion when cell division occurs synchronously. Moreover, when the confinement is removed, we see that the existing coherent motion leads to cell scattering, with bulk cell stiffness and stiffness of cell-cell contacts dictating the invasion pattern. In summary, our study provides an in-depth understanding of the origin of coherent rotation in confined tissues, and extracts useful insights into the influence of various physical parameters on the pattern of such movements.     

Modelling Adaptation to Directional Motion Using the Adelson-Bergen Energy Sensor

The motion energy sensor has been shown to account for a wide range of physiological and psychophysical results in motion detection and discrimination studies. It has become established as the standard computational model for retinal movement sensing in the human visual system. Adaptation effects have been extensively studied in the psychophysical literature on motion perception, and play a crucial role in theoretical debates, but the current implementation of the energy sensor does not provide directly for modelling adaptation-induced changes in output. We describe an extension of the model to incorporate changes in output due to adaptation. The extended model first computes a space-time representation of the output to a given stimulus, and then a RC gain-control circuit (“leaky integrator”) is applied to the time-dependent output. The output of the extended model shows effects which mirror those observed in psychophysical studies of motion adaptation: a decline in sensor output during stimulation, and changes in the relative of outputs of different sensors following this adaptation.     

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.     

A biologically plausible acoustic motion detection neural network

In this paper we present an acoustic motion detection system to be used in a small mobile robot. While the first purpose of the system has been to be a reliable computational implementation, cheap enough to be built in hardware, effort has also been taken to construct a biologically plausible solution. The motion detector consists of a neural network composed of motion-direction sensitive neurons with a preferred direction and a preferred region of the azimuth. The system was designed to produce a higher response when stimulated by motion in the preferred direction than in the null direction and that is in fact what the system does, which means that, as desired, the system can detect motion and distinguish its direction.     

A Common Cortical Circuit Mechanism for Perceptual Categorical Discrimination and Veridical Judgment

Perception involves two types of decisions about the sensory world: identification of stimulus features as analog quantities, or discrimination of the same stimulus features among a set of discrete alternatives. Veridical judgment and categorical discrimination have traditionally been conceptualized as two distinct computational problems. Here, we found that these two types of decision making can be subserved by a shared cortical circuit mechanism. We used a continuous recurrent network model to simulate two monkey experiments in which subjects were required to make either a two-alternative forced choice or a veridical judgment about the direction of random-dot motion. The model network is endowed with a continuum of bell-shaped population activity patterns, each representing a possible motion direction. Slow recurrent excitation underlies accumulation of sensory evidence, and its interplay with strong recurrent inhibition leads to decision behaviors. The model reproduced the monkey's performance as well as single-neuron activity in the categorical discrimination task. Furthermore, we examined how direction identification is determined by a combination of sensory stimulation and microstimulation. Using a population-vector measure, we found that direction judgments instantiate winner-take-all (with the population vector coinciding with either the coherent motion direction or the electrically elicited motion direction) when two stimuli are far apart, or vector averaging (with the population vector falling between the two directions) when two stimuli are close to each other. Interestingly, for a broad range of intermediate angular distances between the two stimuli, the network displays a mixed strategy in the sense that direction estimates are stochastically produced by winner-take-all on some trials and by vector averaging on the other trials, a model prediction that is experimentally testable. This work thus lends support to a common neurodynamic framework for both veridical judgment and categorical discrimination in perceptual decision making.     

Role of the Number of Microtubules in Chromosome Segregation during Cell Division

Faithful segregation of genetic material during cell division requires alignment of chromosomes between two spindle poles and attachment of their kinetochores to each of the poles. Failure of these complex dynamical processes leads to chromosomal instability (CIN), a characteristic feature of several diseases including cancer. While a multitude of biological factors regulating chromosome congression and bi-orientation have been identified, it is still unclear how they are integrated so that coherent chromosome motion emerges from a large collection of random and deterministic processes. Here we address this issue by a three dimensional computational model of motor-driven chromosome congression and bi-orientation during mitosis. Our model reveals that successful cell division requires control of the total number of microtubules: if this number is too small bi-orientation fails, while if it is too large not all the chromosomes are able to congress. The optimal number of microtubules predicted by our model compares well with early observations in mammalian cell spindles. Our results shed new light on the origin of several pathological conditions related to chromosomal instability.     

Possible role of contact following in the generation of coherent motion of Dictyostelium cells

After aggregation by chemotaxis, cells of the cellular slime mold Dictyostelium discoideum form a multicellular structure and show coherent motion such as vortices. Here, we present a mathematical model to explain both aggregation and coherent motion of cells in two-dimensional space. The model incorporates chemotactic response of cells and the cell's property, called "contact following", to follow the other cells with which they are in contact. Analytical study and computer simulation using the model show that with contact following, cells form circular clusters within which cell rotation occurs. Unidirectional cell motion in a long belt of cells is another type of solution of the model. Besides, contact following has an effect to accelerate cell cluster merging. By considering the mechanism of cell movement, possible explanations of contact following are proposed.     

Object Segmentation from Motion Discontinuities and Temporal Occlusions–A Biologically Inspired Model

Background

Optic flow is an important cue for object detection. Humans are able to perceive objects in a scene using only kinetic boundaries, and can perform the task even when other shape cues are not provided. These kinetic boundaries are characterized by the presence of motion discontinuities in a local neighbourhood. In addition, temporal occlusions appear along the boundaries as the object in front covers the background and the objects that are spatially behind it.

Methodology/Principal Findings

From a technical point of view, the detection of motion boundaries for segmentation based on optic flow is a difficult task. This is due to the problem that flow detected along such boundaries is generally not reliable. We propose a model derived from mechanisms found in visual areas V1, MT, and MSTl of human and primate cortex that achieves robust detection along motion boundaries. It includes two separate mechanisms for both the detection of motion discontinuities and of occlusion regions based on how neurons respond to spatial and temporal contrast, respectively. The mechanisms are embedded in a biologically inspired architecture that integrates information of different model components of the visual processing due to feedback connections. In particular, mutual interactions between the detection of motion discontinuities and temporal occlusions allow a considerable improvement of the kinetic boundary detection.

Conclusions/Significance

A new model is proposed that uses optic flow cues to detect motion discontinuities and object occlusion. We suggest that by combining these results for motion discontinuities and object occlusion, object segmentation within the model can be improved. This idea could also be applied in other models for object segmentation. In addition, we discuss how this model is related to neurophysiological findings. The model was successfully tested both with artificial and real sequences including self and object motion.     

Robust velocity computation from a biologically motivated model of motion perception

Current computational models of motion processing in the primate motion pathway do not cope well with image sequences in which a moving pattern is superimposed upon a static texture. The use of non-linear operations and the need for contrast normalization in motion models mean that the separation of the influences of moving and static patterns on the motion computation is not trivial. Therefore, the response to the superposition of static and moving patterns provides an important means of testing various computational strategies. Here we describe a computational model of motion processing in the visual cortex, one of the advantages of which is that it is highly resistant to interference from static patterns.     

Integration of Motion Responses Underlying Directional Motion Anisotropy in Human Early Visual Cortical Areas

Recent imaging studies have reported directional motion biases in human visual cortex when perceiving moving random dot patterns. It has been hypothesized that these biases occur as a result of the integration of motion detector activation along the path of motion in visual cortex. In this study we investigate the nature of such motion integration with functional MRI (fMRI) using different motion stimuli. Three types of moving random dot stimuli were presented, showing either coherent motion, motion with spatial decorrelations or motion with temporal decorrelations. The results from the coherent motion stimulus reproduced the centripetal and centrifugal directional motion biases in V1, V2 and V3 as previously reported. The temporally decorrelated motion stimulus resulted in both centripetal and centrifugal biases similar to coherent motion. In contrast, the spatially decorrelated motion stimulus resulted in small directional motion biases that were only present in parts of visual cortex coding for higher eccentricities of the visual field. In combination with previous results, these findings indicate that biased motion responses in early visual cortical areas most likely depend on the spatial integration of a simultaneously activated motion detector chain.     

Incidental processing of biological motion

The successful detection of biological motion can have important consequences for survival. Previous studies have demonstrated the ease and speed with which observers can extract a wide range of information from impoverished dynamic displays in which only an actor's joints are visible. Although it has often been suggested that such biological motion processing can be accomplished relatively automatically, few studies have directly tested this assumption by using behavioral methods. Here we used a flanker paradigm to assess how peripheral "to-be-ignored" walkers affect the processing of a central target walker. Our results suggest that task-irrelevant dynamic figures cannot be ignored and are processed to a level where they influence behavior. These findings provide the first direct evidence that complex dynamic patterns can be processed incidentally, a finding that may have important implications for cognitive, neurophysiological, and computational models of biological motion processing.