Abstract not found

Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT- /MSTv and MT + /MSTd compute object motion for tracking and self-motion for navigation, respectively. The model retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT + interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT- interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.

Abstract. Humans can recognize biological motion from strongly impoverished stimuli, like point-light displays. Although the neural mechanism underlying this robust perceptual process have not yet been clarified, one possible explanation is that the visual system extracts specific motion features that are suitable for the robust recognition of both normal and degraded stimuli. We present a neural model for biological motion recognition that learns robust mid-level motion features in an unsupervised way using a neurally plausible memory-trace learning rule. Optimal mid-level features were learnt from image motion sequences containing a walker with, or without background motion clutter. After learning of the motion features, the detection performance of the model substantially increases, in particular in presence of clutter. The learned mid-level motion features are characterized by horizontal opponent motion, where this feature type arises more frequently for the training stimuli without motion clutter. The learned features are consistent with recent psychophysical data that indicates that opponent motion might be critical for the detection of point light walkers. 1

Recent psychophysical experiments suggest that humans can recover only relief structure from motion (SFM); i.e., an object’s 3D shape can only be determined up to a stretching transformation along the line of sight. Here we propose a physiologically plausible model for the computation of relief SFM, which is also applicable to the related problem of motion parallax. We assume that the perception of depth from motion is related to the firing of a subset of MT neurons tuned to both velocity and disparity. The model MT neurons are connected to each other laterally to form modulatory interactions. The overall connectivity is such that when a zero-disparity velocity pattern is fed into the system, the most responsive neurons are not those tuned to zero disparity, but instead are those having preferred disparities consistent with the relief structure of the velocity pattern. The model computes the correct relief structure under a wide range of parameters and can also reproduce the SFM illusions involving coaxial cylinders. It is consistent with the psychophysical observation that subjects with stereo impairment are also deficient in perceiving motion parallax, and with the physiological data that the responses of direction- and disparity-tuned MT cells covary with the perceived surface order

Abstract- Humans can recognize biological motion (e.g. a walker) from stimuli with impoverished information, like point-light displays (e.g. a “point-like walker”). Although the neural mechanism underlying such a robust representation remains unclear, a possible explanation is that it is based on specific motion features shared by normal and point-light stimuli. A recent study using image statistics and psychophysics has shown that these features are "opponent-motion " like, which are also congruent with neurophysiological studies. Here we use a plausible algorithm (MeT) to learn mid-level features from motion stimuli within the frame of a model for the recognition of biological motion (MRBM). Features were learnt from motion sequences containing a "walker " in two different situations: with and without cluttered background. Additionally, we use these features to solve a "walker " detection task. Our results showed that in both conditions the MeT algorithm found "opponent-motion " features, which were more present when stimuli contained no background. As we already proved with static stimuli, learning motionspecific ("walker") mid-level features increase detection performance in cluttered-background conditions. 1.

A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discontinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and posterior parietal cortex can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.

Abstract not found