Neural Models of Normal and Abnormal Behavior: What Do Schizophrenia, Parkinsonism, Attention Deficit Disorder, and Depression Have in Common?

Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333

Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs

One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar. For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works.Research supported by the Spanish Government through the Cicyt project ADAS ROAD-EYE (TRA2013-48314-C3-1-R)

Neural Correlates of Reach Planning and Execution

Humans and other primates often interact with the world by reaching and grabbing objects with their hands. This seemingly simple activity is a challenging computational problem that requires the nervous system to transform sensory input into muscle activations that move the hand appropriately through space. This dissertation investigates neural activity in the dorsal premotor cortex of the macaque monkey while simple and complex reaching movements are planned and executed. A novel virtual-reality obstacle-avoidance task is used to decorrelate the direction of the initial segment of the trajectory from the direction of the final target. An unobstructed center-out task is used as a comparison. The firing rates of many neurons are modulated by kinematic factors including hand position and movement direction. During obstacle-avoidance reaching, both the initial segment and final target directions are represented in the firing of dorsal premotor neurons. Population decoders for position, velocity and target direction were built using the indirect optimal linear estimator method, a variant of the population vector algorithm. The decoding model constructed from the direct-reaching task ultimately predicts the direction of movement, not the final target, during the planning period before movement begins. A separate decoding model predicts the target direction when the hand must move elsewhere initially. The time course of neural activity during planning suggests that the two monkeys utilized different preparatory strategies during the obstacle-avoidance task, leading to differences in performance on a subset of trials. A position-based population decoder predicts the hand trajectory during movement, anticipating the real hand position by approximately 200 ms. These findings demonstrate that multiple kinematic parameters of hand movement are represented in dorsal premotor cortex during planning and execution of voluntary reaching behavior. A simple linear decoding scheme based on roughly cosine-tuned spiking activity can extract relevant information from the population of neurons. This work contributes to the overall understanding of the factors that influence dorsal premotor cortical activity during complex reaching movements

How do treadmill speed and terrain visibility influence neuromuscular control of guinea fowl locomotion?

Locomotor control mechanisms must flexibly adapt to both anticipated and unexpected terrain changes to maintain movement and avoid a fall. Recent studies revealed that ground birds alter movement in advance of overground obstacles, but not treadmill obstacles, suggesting context-dependent shifts in the use of anticipatory control. We hypothesized that differences between overground and treadmill obstacle negotiation relate to differences in visual sensory information, which influence the ability to execute anticipatory manoeuvres. We explored two possible explanations: (1) previous treadmill obstacles may have been visually imperceptible, as they were low contrast to the tread, and (2) treadmill obstacles are visible for a shorter time compared with runway obstacles, limiting time available for visuomotor adjustments. To investigate these factors, we measured electromyographic activity in eight hindlimb muscles of the guinea fowl (Numida meleagris, N=6) during treadmill locomotion at two speeds (0.7 and 1.3 m s−1) and three terrain conditions at each speed: (i) level, (ii) repeated 5 cm low-contrast obstacles (90% contrast, black/white). We hypothesized that anticipatory changes in muscle activity would be higher for (1) high-contrast obstacles and (2) the slower treadmill speed, when obstacle viewing time is longer. We found that treadmill speed significantly influenced obstacle negotiation strategy, but obstacle contrast did not. At the slower speed, we observed earlier and larger anticipatory increases in muscle activity and shifts in kinematic timing. We discuss possible visuomotor explanations for the observed context-dependent use of anticipatory strategies

Virtual reality obstacle crossing: adaptation, retention and transfer to the physical world

Virtual reality (VR) paradigms are increasingly being used in movement and exercise sciences with the aim to enhance motor function and stimulate motor adaptation in healthy and pathological conditions. Locomotor training based in VR may be promising for motor skill learning, with transfer of VR skills to the physical world in turn required to benefit functional activities of daily life. This PhD project aims to examine locomotor adaptations to repeated VR obstacle crossing in healthy young adults as well as transfers to the untrained limb and the physical world, and retention potential of the learned skills. For these reasons, the current thesis comprises three studies using controlled VR obstacle crossing interventions during treadmill walking. In the first and second studies we investigated adaptation to crossing unexpectedly appearing virtual obstacles, with and without feedback about crossing performance, and its transfer to the untrained leg. In the third study we investigated transfer of virtual obstacle crossing to physical obstacles of similar size to the virtual ones, that appeared at the same time point within the gait cycle. We also investigated whether the learned skills can be retained in each of the environments over one week. In all studies participants were asked to walk on a treadmill while wearing a VR headset that represented their body as an avatar via real-time synchronised optical motion capture. Participants had to cross virtual and/or physical obstacles with and without feedback about their crossing performance. If applicable, feedback was provided based on motion capture immediately after virtual obstacle crossing. Toe clearance, margin of stability, and lower extremity joint angles in the sagittal plane were calculated for the crossing legs to analyse adaptation, transfer, and retention of obstacle crossing performance. The main outcomes of the first and second studies were that crossing multiple virtual obstacles increased participants’ dynamic stability and led to a nonlinear adaptation of toe clearance that was enhanced by visual feedback about crossing performance. However, independent of the use of feedback, no transfer to the untrained leg was detected. Moreover, despite significant and rapid adaptive changes in locomotor kinematics with repeated VR obstacle crossing, results of the third study revealed limited transfer of learned skills from virtual to physical obstacles. Lastly, despite full retention over one week in the virtual environment we found only partial retention when crossing a physical obstacle while walking on the treadmill. In summary, the findings of this PhD project confirmed that repeated VR obstacle perturbations can effectively stimulate locomotor skill adaptations. However, these are not transferable to the untrained limb irrespective of enhanced awareness and feedback. Moreover, the current data provide evidence that, despite significant adaptive changes in locomotion kinematics with repeated practice of obstacle crossing under VR conditions, transfer to and retention in the physical environment is limited. It may be that perception-action coupling in the virtual environment, and thus sensorimotor coordination, differs from the physical world, potentially inhibiting retained transfer between those two conditions. Accordingly, VR-based locomotor skill training paradigms need to be considered carefully if they are to replace training in the physical world