A neural network model for stroboscopic alternative motion

A neural network which models multistable perception is presented. The network consists of sensor and inner neurons. The dynamics is established by a stochastic neuronal dynamics, a formal Hebb-type coupling dynamics and a resource mechanism that corresponds to saturation effects in perception. From this a system of coupled differential equations is derived and analyzed. Single stimuli are bound to exactly one percept, even in ambiguous situations where multistability occurs. The network exhibits discontinuous as well as continuous phase transitions and models various empirical findings, including the percepts of succession, alternative motion and simultaneity; the percept of oscillation is explained by oscillating percepts at a continuous phase transition. Received: 13 September 1995 / Accepted: 3 June 1996     

Massively parallel implementations of theories for apparent motion

Two solutions for the correspondence problem for long-range motion are investigated. The first is a modification of the Minimal Mapping Theory (S. Ullman: The Interpretation of Visual Motion, MIT Press, Cambridge, 1979) that is implemented by a massively parallel network. In this network, every two units are interconnected, and thus, its convergence is fast and relatively independent of the number of image features. Computer simulations show that our method accounts as well as the Minimal Mapping Theory for apparent-motion phenomena, although some differences exist. Mathematical proofs provide conditions for the convergence of the network. The second 'solution' for the correspondence problem is called the Structural Theory. This theory assumes that the three-dimensional structure of viewed objects does not change fast in time. Then, the theory looks for the correspondence and three-dimensional structure that best fulfill this assumption. A massively parallel network implementation of this theory is also possible. However, its performance is poor due to the high complexity of its solution space. This supports Ullman's (1979) suggestion that the visual system separates the structure-from-motion process into two stages. First, a stage for motion measurement, and then a stage for structure recovery.     

Cell motility as persistent random motion: theories from experiments

Experimental time series for trajectories of motile cells may contain so much information that a systematic analysis will yield cell-type-specific motility models. Here we demonstrate how, using human keratinocytes and fibroblasts as examples. The two resulting models reflect the cells' different roles in the organism, it seems, and show that a cell has a memory of past velocities. They also suggest how to distinguish quantitatively between various surfaces' compatibility with the two cell types.     

Polarization contrast and motion detection

Form and motion perception rely upon the visual system’s capacity to segment the visual scene based upon local differences in luminance or wavelength. It is not clear if polarization contrast is a sufficient basis for motion detection. Here we show that crayfish optomotor responses elicited by the motion of images derived from spatiotemporal variations in e-vector angles are comparable to contrast-elicited responses. Response magnitude increases with the difference in e-vector angles in adjacent segments of the scene and with the degree of polarization but the response is relatively insensitive to the absolute values of e-vector angles that compose the stimulus. The results indicate that polarization contrast can support visual motion detection.     

Visual detection of paradoxical motion in flies

Summary From psychophysics it is known that humans easily perceive motion in Fourier-stimuli in which dots are displaced coherently into one direction. Furthermore, motion can be extracted from Drift-balanced stimuli in which the dots on average have no distinct direction of motion, or even in paradox -motion stimuli where the dots are displaced opposite to the perceived direction of motion. Whereas Fourier-motion can be explained by very basic motion detectors and nonlinear preprocessing of the input can account for the detection of Drift-balanced motion, a hierarchical model with two layers of motion detectors was proposed to explain the perception of -motion. The well described visual system of the fly allows to investigate whether these complex motion stimuli can be detected in a comparatively simple brain.The detection of such motion stimuli was analyzed for various random-dot cinematograms with extracellular recordings from the motion-sensitive Hl-neuron in the third visual ganglion of the blowfly Calliphora erythrocephala. The results were compared to computer-simulations of a hierarchical model of motion detector networks.For Fourier- and Drift-balanced motion stimuli, the Hl-neuron responds directionally selective to the moving object, whereas for -motion stimuli, the preferred direction is given by the dot displacement. Assuming nonlinear preprocessing of the detector input, such as a half-wave rectification, elementary motion detectors of the correlation type can account for these results.Abbreviations EMD elementary motion detector     

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.     

The first steps in Drosophila motion detection

The visual system, with its ability to perceive motion, is crucial for most animals to walk or fly steadily. Theoretical models of motion detection exist, but the underlying cellular mechanisms are still poorly understood. In this issue of Neuron, Rister and colleagues dissect the function of neuronal subtypes in the optic lobe of Drosophila to reveal their role in motion detection.     

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.     

Matched filtering in motion detection and discrimination

When humans detect and discriminate visual motion, some neural mechanism extracts the motion information that is embedded in the noisy spatio-temporal stimulus. We show that an ideal mechanism in a motion discrimination experiment cross-correlates the received waveform with the signals to be discriminated. If the human visual system uses such a cross-correlator mechanism, discrimination performance should depend on the cross-correlation between the two signals. Manipulations of the signals' cross-correlation using differences in the speed and phase of moving gratings produced the predicted changes in the performance of human observers. The cross-correlator's motion performance improves linearly as contrast increases and human performance is similar. The ideal cross-correlator can be implemented by passing the stimulus through linear spatio-temporal filters matched to the signals. We propose that directionally selective simple cells in the striate cortex serve as matched filters during motion detection and discrimination.     

A genetic push to understand motion detection

Two articles in this issue of Neuron (Eichner et?al. and Clark et?al.) attack the problem of explaining how neuronal hardware in Drosophila implements the Reichardt motion detector, one of the most famous computational models in neuroscience, which has proven intractable up to now.     

Environmental motion delays the detection of movement-based signals

Animal signals are constrained by the environment in which they are transmitted and the sensory systems of receivers. Detection of movement-based signals is particularly challenging against the background of wind-blown plants. The Australian lizard Amphibolurus muricatus has recently been shown to compensate for greater plant motion by prolonging the introductory tail-flicking component of its movement-based display. Here I demonstrate that such modifications to signal structure are useful because environmental motion lengthens the time lizard receivers take to detect tail flicks. The spatio-temporal properties of animal signals and environmental motion are thus sufficiently similar to make signal detection more difficult. Environmental motion, therefore, must have had an influence on the evolution of movement-based signals and motion detection mechanisms.     

Representation of motion onset and offset in an augmented Barlow-Levick model of motion detection

Kinetic occlusion produces discontinuities in the optic flow field, whose perception requires the detection of an unexpected onset or offset of otherwise predictably moving or stationary contrast patches. Many cells in primate visual cortex are directionally selective for moving contrasts, and recent reports suggest that this selectivity arises through the inhibition of contrast signals moving in the cells’ null direction, as in the rabbit retina. This nulling inhibition circuit (Barlow-Levick) is here extended to also detect motion onsets and offsets. The selectivity of extended circuit units, measured as a peak evidence accumulation response to motion onset/offset compared to the peak response to constant motion, is analyzed as a function of stimulus speed. Model onset cells are quiet during constant motion, but model offset cells activate during constant motion at slow speeds. Consequently, model offset cell speed tuning is biased towards higher speeds than onset cell tuning, similarly to the speed tuning of cells in the middle temporal area when exposed to speed ramps. Given a population of neurons with different preferred speeds, this asymmetry addresses a behavioral paradox—why human subjects in a simple reaction time task respond more slowly to motion offsets than onsets for low speeds, even though monkey neuron firing rates react more quickly to the offset of a preferred stimulus than to its onset.     

Communicative interactions improve visual detection of biological motion

Background

In the context of interacting activities requiring close-body contact such as fighting or dancing, the actions of one agent can be used to predict the actions of the second agent [1]. In the present study, we investigated whether interpersonal predictive coding extends to interactive activities – such as communicative interactions - in which no physical contingency is implied between the movements of the interacting individuals.

Methodology/Principal Findings

Participants observed point-light displays of two agents (A and B) performing separate actions. In the communicative condition, the action performed by agent B responded to a communicative gesture performed by agent A. In the individual condition, agent A''s communicative action was substituted with a non-communicative action. Using a simultaneous masking detection task, we demonstrate that observing the communicative gesture performed by agent A enhanced visual discrimination of agent B.

Conclusions/Significance

Our finding complements and extends previous evidence for interpersonal predictive coding, suggesting that the communicative gestures of one agent can serve as a predictor for the expected actions of the respondent, even if no physical contact between agents is implied.     

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 stochastic model for the detection of coherent motion

A computational model is presented for the detection of coherent motion based on template matching and hidden Markov models. The premise of this approach is that the growth in detection sensitivity is greater for coherent motion of structured forms than for random coherent motion. In this preliminary study, a recent experiment was simulated with the model and the results are shown to be in agreement with the above premise. This model can be extended to be part of a more complex and elaborate computational visual system.     

Application of motion analysis system in pre-impact fall detection

The purpose of this study is to investigate unique features of body segments in fall and activities of daily living (ADL) to make automatic detection of fall in its descending phase before the impact. Thus, fall-related injuries can be prevented or reduced by deploying feedback systems before the impact. In this study, the authors propose the following hypothesis: (1) thigh segment normally does not go beyond certain threshold angle to forward and sideways directions in ADL and (2) even if it does, the angular characteristics measured at torso and thigh differ from one another in ADL whereas in the case of fall, they become congruent. These two factors can be used to distinguish fall from ADL in its inception. Vicon 3-D motion analysis system was used in this study. High level of correlation between thigh and torso segments (corr > 0.99) was found for fall activities and low correlation coefficients (mean corr for lateral movements is 0.2338 and for sagittal movements is -0.665) were observed in ADL. By applying the hypothesis, all simulated falls could be detected with no false alarms and around 700ms lead-time before the impact was achieved in pre-impact fall detection. It is the longest lead-time obtained so far in pre-impact fall detection.     

A stroboscopic technique for measuring Daphnia heart-beat rate

The variation of Daphnia heart-beat rate with temperature is described. The temperature can be changed and monitored continuously while the heartbeat rate is measured using stroboscopic techniques.     

Tracing eye movements in various types of stroboscopic stimulation

Relationship between eye ability to perceive smooth motion under stroboscopic stimulation on forward motion (from which stationary positions of the object) in the plane perpendicular to the look line was found. For diagnostics and occupational selection it is suggested to carry out stimulation in several directions thus obtaining additional information about the visual system of the person under test.