Neural Dynamics of Motion Grouping: From Aperture Ambiguity to Object Speed and Direction

A neural network model of visual motion perception and speed discrimination is developed to simulate data concerning the conditions under which components of moving stimuli cohere or not into a global direction of motion, as in barberpole and plaid patterns (both Type 1 and Type 2). The model also simulates how the perceived speed of lines moving in a prescribed direction depends upon their orientation, length, duration, and contrast. Motion direction and speed both emerge as part of an interactive motion grouping or segmentation process. The model proposes a solution to the global aperture problem by showing how information from feature tracking points, namely locations from which unambiguous motion directions can be computed, can propagate to ambiguous motion direction points, and capture the motion signals there. The model does this without computing intersections of constraints or parallel Fourier and non-Fourier pathways. Instead, the model uses orientationally-unselective cell responses to activate directionally-tuned transient cells. These transient cells, in turn, activate spatially short-range filters and competitive mechanisms over multiple spatial scales to generate speed-tuned and directionally-tuned cells. Spatially long-range filters and top-down feedback from grouping cells are then used to track motion of featural points and to select and propagate correct motion directions to ambiguous motion points. Top-down grouping can also prime the system to attend a particular motion direction. The model hereby links low-level automatic motion processing with attention-based motion processing. Homologs of model mechanisms have been used in models of other brain systems to simulate data about visual grouping, figure-ground separation, and speech perception. Earlier versions of the model have simulated data about short-range and long-range apparent motion, second-order motion, and the effects of parvocellular and magnocellular LGN lesions on motion perception.Office of Naval Research (N00014-920J-4015, N00014-91-J-4100, N00014-95-1-0657, N00014-95-1-0409, N00014-91-J-0597); Air Force Office of Scientific Research (F4620-92-J-0225, F49620-92-J-0499); National Science Foundation (IRI-90-00530

The Perception of Globally Coherent Motion

How do human observers perceive a coherent pattern of motion from a disparate set of local motion measures? Our research has examined how ambiguous motion signals along straight contours are spatially integrated to obtain a globally coherent perception of motion. Observers viewed displays containing a large number of apertures, with each aperture containing one or more contours whose orientations and velocities could be independently specified. The total pattern of the contour trajectories across the individual apertures was manipulated to produce globally coherent motions, such as rotations, expansions, or translations. For displays containing only straight contours extending to the circumferences of the apertures, observers' reports of global motion direction were biased whenever the sampling of contour orientations was asymmetric relative to the direction of motion. Performance was improved by the presence of identifiable features, such as line ends or crossings, whose trajectories could be tracked over time. The reports of our observers were consistent with a pooling process involving a vector average of measures of the component of velocity normal to contour orientation, rather than with the predictions of the intersection-of-constraints analysis in velocity space.Air Force Office of Scientific Research (90-0175, 89-0016); National Science Foundation, Office of Naval Research, Air Force Office of Scientific Research (BNS-8908426

Efficient spiking neural network model of pattern motion selectivity in visual cortex

Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction- selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40∈×∈40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available. © 2014 Springer Science+Business Media New York

Computing motion in the primate's visual system

Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We will show how one well-known gradient-based computer algorithm for estimating visual motion can be implemented within the primate's visual system. This relaxation algorithm computes the optical flow field by minimizing a variational functional of a form commonly encountered in early vision, and is performed in two steps. In the first stage, local motion is computed, while in the second stage spatial integration occurs. Neurons in the second stage represent the optical flow field via a population-coding scheme, such that the vector sum of all neurons at each location codes for the direction and magnitude of the velocity at that location. The resulting network maps onto the magnocellular pathway of the primate visual system, in particular onto cells in the primary visual cortex (V1) as well as onto cells in the middle temporal area (MT). Our algorithm mimics a number of psychophysical phenomena and illusions (perception of coherent plaids, motion capture, motion coherence) as well as electrophysiological recordings. Thus, a single unifying principle ‘the final optical flow should be as smooth as possible’ (except at isolated motion discontinuities) explains a large number of phenomena and links single-cell behavior with perception and computational theory

Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception

Choosing an appropriate set of stimuli is essential to characterize the response of a sensory system to a particular functional dimension, such as the eye movement following the motion of a visual scene. Here, we describe a framework to generate random texture movies with controlled information content, i.e., Motion Clouds. These stimuli are defined using a generative model that is based on controlled experimental parametrization. We show that Motion Clouds correspond to dense mixing of localized moving gratings with random positions. Their global envelope is similar to natural-like stimulation with an approximate full-field translation corresponding to a retinal slip. We describe the construction of these stimuli mathematically and propose an open-source Python-based implementation. Examples of the use of this framework are shown. We also propose extensions to other modalities such as color vision, touch, and audition

Neural Dynamics of Motion Processing and Speed Discrimination

A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size-speed correlation, can be derived from the simplest mechanisms whereby activations of multiple spatially short-range filters of different size are transformed into speed-tuned cell responses. These mechanisms use transient cell responses to moving stimuli, output thresholds that covary with filter size, and competition. These mechanisms are proposed to occur in the Vl→7 MT cortical processing stream. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination can be affected by stimulus contrast, duration, dot density and spatial frequency. Model motion mechanisms are analogous to mechanisms that have been used to model 3-D form and figure-ground perception. The model forms the front end of a larger motion processing system that has been used to simulate how global motion capture occurs, and how spatial attention is drawn to moving forms. It provides a computational foundation for an emerging neural theory of 3-D form and motion perception.Office of Naval Research (N00014-92-J-4015, N00014-91-J-4100, N00014-95-1-0657, N00014-95-1-0409, N00014-94-1-0597, N00014-95-1-0409); Air Force Office of Scientific Research (F49620-92-J-0499); National Science Foundation (IRI-90-00530

Effect of contrast on the perception of direction of a moving pattern

A series of experiments examining the effect of contrast on the perception of moving plaids was performed to test the hypothesis that the human visual system determines the direction of a moving plaid in a two-staged process: decomposition into component motion followed by application of the intersection-of-contraints rule. Although there is recent evidence that the first tenet of the hypothesis is correct, i.e., that plaid motion is initially decomposed into the motion of the individual grating components, the nature of the second-stage combination rule has not yet been established. It was found that when the gratings within the plaid are of different contrast the preceived direction is not predicted by the intersection-of-constraints rule. There is a strong (up to 20 deg) bias in the direction of the higher-constrast grating. A revised model, which incorporates a contrast-dependent weighting of perceived grating speed as observed for one-dimensional patterns, can quantitatively predict most of the results. The results are then discussed in the context of various models of human visual motion processing and of physiological responses of neurons in the primate visual system

Neural Dynamics of Motion Perception: Direction Fields, Apertures, and Resonant Grouping

A neural network model of global motion segmentation by visual cortex is described. Called the Motion Boundary Contour System (BCS), the model clarifies how ambiguous local movements on a complex moving shape are actively reorganized into a coherent global motion signal. Unlike many previous researchers, we analyse how a coherent motion signal is imparted to all regions of a moving figure, not only to regions at which unambiguous motion signals exist. The model hereby suggests a solution to the global aperture problem. The Motion BCS describes how preprocessing of motion signals by a Motion Oriented Contrast Filter (MOC Filter) is joined to long-range cooperative grouping mechanisms in a Motion Cooperative-Competitive Loop (MOCC Loop) to control phenomena such as motion capture. The Motion BCS is computed in parallel with the Static BCS of Grossberg and Mingolla (1985a, 1985b, 1987). Homologous properties of the Motion BCS and the Static BCS, specialized to process movement directions and static orientations, respectively, support a unified explanation of many data about static form perception and motion form perception that have heretofore been unexplained or treated separately. Predictions about microscopic computational differences of the parallel cortical streams V1 --> MT and V1 --> V2 --> MT are made, notably the magnocellular thick stripe and parvocellular interstripe streams. It is shown how the Motion BCS can compute motion directions that may be synthesized from multiple orientations with opposite directions-of-contrast. Interactions of model simple cells, complex cells, hypercomplex cells, and bipole cells are described, with special emphasis given to new functional roles in direction disambiguation for endstopping at multiple processing stages and to the dynamic interplay of spatially short-range and long-range interactions.Air Force Office of Scientific Research (90-0175); Defense Advanced Research Projects Agency (90-0083); Office of Naval Research (N00014-91-J-4100

How Is a Moving Target Continuously Tracked Behind Occluding Cover?

Office of Naval Research (N00014-95-1-0657, N00014-95-1-0409

Information processing in visual systems

One of the goals of neuroscience is to understand how animals perceive sensory information. This thesis focuses on visual systems, to unravel how neuronal structures process aspects of the visual environment. To characterise the receptive field of a neuron, we developed spike-triggered independent component analysis. Alongside characterising the receptive field of a neuron, this method provides an insight into its underlying network structure. When applied to recordings from the H1 neuron of blowflies, it accurately recovered the sub-structure of the neuron. This sub-structure was studied further by recording H1's response to plaid stimuli. Based on the response, H1 can be classified as a component cell. We then fitted an anatomically inspired model to the response, and found the critical component to explain H1's response to be a sigmoid non-linearity at output of elementary movement detectors. The simpler blowfly visual system can help us understand elementary sensory information processing mechanisms. How does the more complex mammalian cortex implement these principles in its network? To study this, we used multi-electrode arrays to characterise the receptive field properties of neurons in the visual cortex of anaesthetised mice. Based on these recordings, we estimated the cortical limits on the performance of a visual task; the behavioural performance observed by Prusky and Douglas (2004) is within these limits. Our recordings were carried out in anaesthetised animals. During anaesthesia, cortical UP states are considered "fragments of wakefulness" and from simultaneous whole-cell and extracellular recordings, we found these states to be revealed in the phase of local field potentials. This finding was used to develop a method of detecting cortical state based on extracellular recordings, which allows us to explore information processing during different cortical states. Across this thesis, we have developed, tested and applied methods that help improve our understanding of information processing in visual systems