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A collision avoidance model based on the Lobula giant movement detector (LGMD) neuron of the locust

In insects, we can find very complex and compact neural structures that are task specific. These neural structures allow them to perform complex tasks such as visual navigation, including obstacle avoidance, landing, self-stabilization, etc. Obstacle avoidance is fundamental for successful navigation, and it can be combined with more systems to make up more complex behaviors. In this paper, we present a model for collision avoidance based on the Lobula giant movement detector (LGMD) cell of the locust. This is a wide-field visual neuron that responds to looming stimuli and that can trigger avoidance reactions whenever a rapidly approaching object is detected. Here, we present result based on both an offline study of the model and its application to a flying robot.

Real-time position reconstruction with hippocampal place cells.

Brain–computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rats trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rats position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5–6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral–neuronal feedback loops or for implementing neuroprosthetic control.

Using a multi-task adaptive VR system for upper limb rehabilitation in the acute phase of stroke

Nowadays, stroke has become one the main causes of adult disability leading to life-lasting effects, including motor and cognitive deficits. Here we explore the benefits of the use of virtual reality (VR) for the rehabilitation of motor deficits following stroke. We have developed the rehabilitation gaming system (RGS), a VR-based apparatus designed for the treatment of the upper extremities. The RGS is a multi-level adaptive system that provides a task oriented training of graded complexity that is online adjusted to the capabilities of the patients. We show results from an ongoing study that evaluates the impact of this system on the recovery of patients in the acute phase of stroke (n=14). The results suggest that the system induces a sustained improvement during treatment, with observed benefits in the performance of activities of daily living.

A Biologically Based Flight Control System for a Blimp-based UAV

Autonomous navigation in 2D and 3D environments has been studied for a long time. Navigating within a 3D environment is very challenging for both animals and robots and a variety of sensors are used to solve this task ranging from vision or a simple gyro or compass to GPS. The principal tasks for 3D autonomous navigation are course stabilization, altitude and drift control, and collision avoidance. Using this basis, some features can be easily added like aerial mapping, object recognition, homing strategies or takeoff and landing. Here we present a biologically based control layer for an Unmanned Aerial Vehicle (UAV) that provides course stabilization, altitude and drift control, and collision avoidance. The properties of this neuronal control system are evaluated using a flying robot.

Closing the Loop in Exergaming - Health Benefits of Biocybernetic Adaptation in Senior Adults

Exergames help senior players to get physically active by promoting fun and enjoyment while exercising. However, most exergames are not designed to produce recommended levels of exercise that elicit adequate physical responses for optimal training in the aged population. In this project, we developed physiological computing technologies to overcome this issue by making real-time adaptations in a custom exergame based on recommendations for targeted heart rate (HR) levels. This biocybernetic adaptation was evaluated against conventional cardiorespiratory training in a group of active senior adults through a floor-projected exergame and a smartwatch to record HR data. Results showed that the physiologically-augmented exergame leads players to exert around 40% more time in the recommended HR levels, compared to the conventional training, avoiding over exercising and maintaining good enjoyment levels. Finally, we made available our biocybernetic adaptation software tool to enable the creation of physiological adaptive videogames, permitting the replication of our study.