Elderly exhibit accumulating deficits in visual motion perception, which is critical for humans to interact with their environment. Previous studies have suggested that aging generally reduces neuronal inhibition in the visual system. Here, we investigated how aging affects the local intra-cortical inhibition using a motion direction discrimination task based on the motion repulsion phenomenon. Motion repulsion refers to the phenomenon by which observers overestimate the perceived angle when two superimposed dot patterns are moving at an acute angle. The misperception has been interpreted as local mutual inhibition between nearby direction-tuned neurons within the same cortical area. We found that elderly exhibited much stronger motion repulsion than young adults. We then compared this effect to how aging affects the global inter-cortical inhibition by adopting the surround suppression paradigm previously used by Betts et al. (2005). We found that elderly showed less change in the discrimination threshold when the size of a high-contrast drifting Gabor was increased, indicating reduced surround suppression compared to young adults. Our results indicate that aging may not always lead to a decrease of neuronal inhibition in the visual system. These distinct effects of aging on inhibitory functions might be one of the reasons that elderly people often exhibit deficits of motion perception in a real-world situation.
Previous studies have documented that the perception of self-motion direction can be extracted from the patterns of image motion on the retina (also termed optic flow). Self-motion perception remains stable even when the optic-flow information is distorted by concurrent gaze shifts from body/eye rotations. This has been interpreted that extraretinal signals-efference copies of eye/body movements-are involved in compensating for retinal distortions. Here, we tested an alternative hypothesis to the extraretinal interpretation. We hypothesized that accurate self-motion perception can be achieved from a purely optic-flow-based visual strategy acquired through experience, independent of extraretinal mechanism. To test this, we asked human subjects to perform a self-motion direction discrimination task under normal optic flow (fixation condition) or distorted optic flow resulted from either realistic (pursuit condition) or simulated (simulated condition) eye movements. The task was performed either without (pre-and posttraining) or with (during training) the feedback about the correct answer. We first replicated the previous observation that before training, direction perception was greatly impaired in the simulated condition where the optic flow was distorted and extraretinal eye movement signals were absent. We further showed that after a few training sessions, the initial impairment in direction perception was gradually improved. These results reveal that behavioral training can enforce the exploitation of retinal cues to compensate for the distortion, without the contribution from the extraretinal signals. Our results suggest that self-motion perception is a flexible and adaptive process which might depend on neural plasticity in relevant cortical areas.
Redundant muscles in human-like musculoskeletal robots provide additional dimensions to the solution space. Consequently, the computation of muscle excitations remains an open question. Conventional methods like dynamic optimization and reinforcement learning usually have high computational costs or unstable learning processes when applied to a complex musculoskeletal system. Inspired by human learning, we propose a phased target learning framework that provides different targets to learners at varying levels, to guide their training process and to avoid local optima. By introducing an extra layer of neurons reflecting a preference, we improve the Q-network method to generate continuous excitations. In addition, based on information transmission in the human nervous system, two kinds of biological noise sources are introduced into our framework to enhance exploration over the solution space. Tracking experiments based on a simplified musculoskeletal arm model indicate that under guidance of phased targets, the proposed framework prevents divergence of excitations, thus stabilizing training. Moreover, the enhanced exploration of solutions results in smaller motion errors. The phased target learning framework can be expanded for general-purpose reinforcement learning, and it provides a preliminary interpretation for modeling the mechanisms of human motion learning.
As a harmless detection method, terahertz has become a new trend in security detection. However, there are inherent problems such as the low quality of the images collected by terahertz equipment and the insufficient detection accuracy of dangerous goods. This work advances BiFPN at the neck of YOLOv5 of the deep learning model as a mechanism to improve low resolution. We also perform transfer learning, thereby fine-tuning the pre-training weight of the backbone for migration learning in our model. Results from experimental analysis reveal that mAP@0.5 and mAP@0.5:0.95 values witness a percentage increase of 0.2% and 1.7%, respectively, attesting to the superiority of the proposed model to YOLOv5, which is the state-of-the-art model in object detection.
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