Delayed escape behavior requires claustral activity.

Animals are known to exhibit innate and learned forms of defensive behaviors, but it is unclear whether animals can escape through methods other than these forms. In this study, we develop the delayed escape task, in which male rats temporarily hold the information required for future escape, and we demonstrate that this task, in which the subject extrapolates from past experience without direct experience of its behavioral outcome, does not fall into either of the two forms of behavior. During the holding period, a subset of neurons in the rostral-to-striatum claustrum (rsCla), only when pooled together, sustain enhanced population activity without ongoing sensory stimuli. Transient inhibition of rsCla neurons during the initial part of the holding period produces prolonged inhibition of the enhanced activity. The transient inhibition also attenuates the delayed escape behavior. Our data suggest that the rsCla activity bridges escape-inducing stimuli to the delayed onset of escape.

Determination of Vehicle Trajectory through Optimization of Vehicle Bounding Boxes Using a Convolutional Neural Network.

In this manuscript, a new method for the determination of vehicle trajectories using an optimal bounding box for the vehicle is developed. The vehicle trajectory is extracted using images acquired from a camera installed at an intersection based on a convolutional neural network (CNN). First, real-time vehicle object detection is performed using the YOLOv2 model, which is one of the most representative object detection algorithms based on CNN. To overcome the inaccuracy of the vehicle location extracted by YOLOv2, the trajectory was calibrated using a vehicle tracking algorithm such as a Kalman filter and intersection-over-union (IOU) tracker. In particular, we attempted to correct the vehicle trajectory by extracting the center position based on the geometric characteristics of a moving vehicle according to the bounding box. The quantitative and qualitative evaluations indicate that the proposed algorithm can detect the trajectories of moving vehicles better than the conventional algorithm. Although the center points of the bounding boxes obtained using the existing conventional algorithm are often outside of the vehicle due to the geometric displacement of the camera, the proposed technique can minimize positional errors and extract the optimal bounding box to determine the vehicle location.