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The role of parvalbumin (PV)-positive interneurons in ocular dominance plasticity (ODP) has been a point of contention; here PV-positive cells are shown to initiate competitive periods of plasticity during the critical periods of eye development when ODP occurs, and transient reductions in inhibitory firing from PV-positive cells provides a return to normal firing rates in excitatory neurons, a key step in ODP progression. Influence of sensory experience on cortical microcircuitry It has long been known that early sensory experience can significantly affect the development and maturation of neural circuitry. One well-studied example of this is ocular dominance plasticity (ODP), in which loss of vision in one eye results in a reduction of cortical responsiveness to that eye. The mechanisms underlying the manifestation of ODP and the roles of inhibitory neurons in its expression have been a point of contention. Kuhlman et al . now show that parvalbumin-positive (PV + ) interneurons initiate competitive periods of plasticity during the critical periods of development when ODP occurs. Transient reductions in inhibitory firing from PV + cells provides for a return to normal firing rates in excitatory neurons, a key step in the progression of ODP in the adolescent cortex. Early sensory experience instructs the maturation of neural circuitry in the cortex 1 , 2 . This has been studied extensively in the primary visual cortex, in which loss of vision to one eye permanently degrades cortical responsiveness to that eye 3 , 4 , a phenomenon known as ocular dominance plasticity (ODP). Cortical inhibition mediates this process 4 , 5 , 6 , but the precise role of specific classes of inhibitory neurons in ODP is controversial. Here we report that evoked firing rates of binocular excitatory neurons in the primary visual cortex immediately drop by half when vision is restricted to one eye, but gradually return to normal over the following twenty-four hours, despite the fact that vision remains restricted to one eye. This restoration of binocular-like excitatory firing rates after monocular deprivation results from a rapid, although transient, reduction in the firing rates of fast-spiking, parvalbumin-positive (PV) interneurons, which in turn can be attributed to a decrease in local excitatory circuit input onto PV interneurons. This reduction in PV-cell-evoked responses after monocular lid suture is restricted to the critical period for ODP and appears to be necessary for subsequent shifts in excitatory ODP. Pharmacologically enhancing inhibition at the time of sight deprivation blocks ODP and, conversely, pharmacogenetic reduction of PV cell firing rates can extend the critical period for ODP. These findings define the microcircuit changes initiating competitive plasticity during critical periods of cortical development. Moreover, they show that the restoration of evoked firing rates of layer 2/3 pyramidal neurons by PV-specific disinhibition is a key step in the progression of ODP.

Developmental myelination is a protracted process in the mammalian brain 1 . One theory for why oligodendrocytes mature so slowly posits that myelination may stabilize neuronal circuits and temper neuronal plasticity as animals age 2 – 4 . We tested this theory in the visual cortex, which has a well-defined critical period for experience-dependent neuronal plasticity 5 . During adolescence, visual experience modulated the rate of oligodendrocyte maturation in visual cortex. To determine whether oligodendrocyte maturation in turn regulates neuronal plasticity, we genetically blocked oligodendrocyte differentiation and myelination in adolescent mice. In adult mice lacking adolescent oligodendrogenesis, a brief period of monocular deprivation led to a significant decrease in visual cortex responses to the deprived eye, reminiscent of the plasticity normally restricted to adolescence. This enhanced functional plasticity was accompanied by a greater turnover of dendritic spines and coordinated reductions in spine size following deprivation. Furthermore, inhibitory synaptic transmission, which gates experience-dependent plasticity at the circuit level, was diminished in the absence of adolescent oligodendrogenesis. These results establish a critical role for oligodendrocytes in shaping the maturation and stabilization of cortical circuits and support the concept of developmental myelination acting as a functional brake on neuronal plasticity. Through genetic blocking of oligodendrocyte differentiation and myelination in adolescent mice, we demonstrate that oligodendrocytes have a critical role in shaping the maturation and stabilization of visual cortical circuits.

•Perception learning causes a transient increase in brain grey matter volume detectable by MRI.•This learning results in pronounced changes of neuronal dendrites and an increase in the number of dendritic spines.•Structural neuronal plasticity is associated with a reorganization and transient swelling of astrocytes.•Brain volume and astrocyte volume return to baseline post-learning, with a persistent increase in the number of mature spines. Volumetric magnetic resonance imaging studies have shown that intense learning can be associated with grey matter volume increases in the adult brain. The underlying mechanisms are poorly understood. Here we used monocular deprivation in rats to analyze the mechanisms underlying use-dependent grey matter increases. Optometry for quantification of visual acuity was combined with volumetric magnetic resonance imaging and microscopic techniques in longitudinal and cross-sectional studies. We found an increased spatial vision of the open eye which was associated with a transient increase in the volumes of the contralateral visual and lateral entorhinal cortex. In these brain areas dendrites of neurons elongated, and there was a strong increase in the number of spines, the targets of synapses, which was followed by spine maturation and partial pruning. Astrocytes displayed a transient pronounced swelling and underwent a reorganization of their processes. The use-dependent increase in grey matter corresponded predominantly to the swelling of the astrocytes. Experience-dependent increase in brain grey matter volume indicates a gain of structure plasticity with both synaptic and astrocyte remodeling.

As the global economy continues to expand and energy demand increases, the size of power transmission networks continues to grow, making the safety monitoring of transmission towers increasingly important. To address the accuracy deficiencies of existing technologies in predicting external damage risks to transmission towers, this study proposes a real-time spatial distance measurement method based on monocular vision. The method first uses a Transformer network to optimize the distribution of pseudo point clouds and designs a 3D monocular vision distance measurement method based on LiDAR. Through validation on the KITTI 3D object detection dataset, the method achieved an average detection accuracy increase of 10.71% in easy scenarios and 2.18% to 7.85% in difficult scenarios compared to other methods. In addition, this study introduced a foreground target depth optimization method based on a 2D target detector and geometric constraints, which further improved the accuracy of 3D target detection. The innovation of the study is the optimization of the pseudo point cloud distribution using the transformer network, which effectively captured the global dependencies and improved the global consistency and local detail accuracy of the pseudo point clouds. The method proposed in the study provides a new approach for intelligent detection and recognition of power transmission lines, and provides a positive impetus for the fields of power engineering and computer vision.

Autonomous vehicles, such as Unmanned Aerial Vehicles (UAVs), have the potential to completely reshape various industries such as parcel delivery, agriculture, surveillance, monitoring, and search-and-rescue missions. Consequently, the demand for safe, cost-effective, and intelligent navigation systems is crucial to ensure reliable performance in complex and dynamic environments. In this study, we propose a novel vision-based UAV navigation method that integrates depthmap estimation with a Vision-Language Model (VLM) for efficient obstacle avoidance and path planning. The system processes RGB images captured by the UAV, transforming them into depth maps using DepthAnything-V2, a powerful zero-shot depth estimator. These depth maps are then analyzed by the VLM, which detects nearby obstacles and plans avoidance maneuvers. We have explored the Gemini-flash and GPT-4o models as VLM in our study. A fully connected network integrates the VLM output with the UAV’s relative heading angle to predict the optimal course of action, enabling the UAV to dynamically navigate complex environments toward its target. The system’s effectiveness is validated through simulations in AirSim using Blocks and the Downtown West environment. The UAV consistently reaches its destination, avoiding obstacles and achieving a near-perfect task completion rate of 0.98. By eliminating the need for costly sensors such as LiDAR and operating without pre-existing maps, our solution provides a cost-efficient, generalizable approach to real-time UAV navigation, especially in unfamiliar or dynamic settings, and highlights emerging trends in autonomous systems research that utilize VLMs.

Unlike ballistic arm movements such as reaching, the contribution of depth information to the performance of manual tracking movements is unclear. Thus, to understand how the brain handles information, we investigated how a required movement along the depth axis would affect behavioral tracking performance, postulating that it would be affected by the amount of depth movement. We designed a visually guided planar tracking task that requires movement on three planes with different depths: a fronto-parallel plane called ROT (0), a sagittal plane called ROT (90), and a plane rotated by 45° with respect to the sagittal plane called ROT (45). Fifteen participants performed a circular manual tracking task under binocular and monocular visions in a three-dimensional (3D) virtual reality space. As a result, under binocular vision, ROT (90), which required the largest depth movement among the tasks, showed the greatest error in 3D. Similarly, the errors (deviation from the target path) on the depth axis revealed significant differences among the tasks. Under monocular vision, significant differences in errors were observed only on the lateral axis. Moreover, we observed that the errors in the lateral and depth axes were proportional to the required movement on these axes under binocular vision and confirmed that the required depth movement under binocular vision determined depth error independent of the other axes. This finding implies that the brain may independently process binocular vision information on each axis. Meanwhile, the required depth movement under monocular vision was independent of performance along the depth axis, indicating an intractable behavior. Our findings highlight the importance of handling depth movement, especially when a virtual reality situation, involving tracking tasks, is generated.

In normally sighted adult volunteers, applying a monocular patch for a few hours produces a short-term shift of ocular dominance in favor of the deprived eye—a phenomenon often interpreted as a form of homeostatic plasticity. We recently showed that the same effect can be elicited without eye-patching, by delaying the image in one eye (by 333 ms) over a 1 h period, during which participants engaged in a visuomotor coordination task; at the end of this period, ocular dominance shifted in favor of the delayed eye. Here we extended these findings, showing that passive exposure to the dichoptic replay of the same video with the same monocular delay did not affect ocular dominance. Moreover, we showed that the ocular dominance shift elicited by monocular delay during goal-directed actions had the same size as the effect of monocular deprivation, achieved by replacing the delayed image with a homogeneous gray screen, and that the two effects were correlated across participants. These results suggest that homeostatic plasticity is gated by a mismatch between vision in one eye and its multimodal context, and it is not necessarily linked with visual deprivation.

•We unveiled the impact of temporary MD on visual and audio-visual processing.•MD enhanced visual excitability for the deprived eye.•MD boosted neural responses to audio-visual events for the non-deprived eye.•Analyses of auditory processing revealed crossmodal effects following MD.•Short-term MD primarily affects induced, non-phase-locked, oscillatory activity. A brief period of monocular deprivation (MD) induces short-term plasticity of the adult visual system. Whether MD elicits neural changes beyond visual processing is yet unclear. Here, we assessed the specific impact of MD on neural correlates of multisensory processes. Neural oscillations associated with visual and audio-visual processing were measured for both the deprived and the non-deprived eye. Results revealed that MD changed neural activities associated with visual and multisensory processes in an eye-specific manner. Selectively for the deprived eye, alpha synchronization was reduced within the first 150 ms of visual processing. Conversely, gamma activity was enhanced in response to audio-visual events only for the non-deprived eye within 100–300 ms after stimulus onset. The analysis of gamma responses to unisensory auditory events revealed that MD elicited a crossmodal upweight for the non-deprived eye. Distributed source modeling suggested that the right parietal cortex played a major role in neural effects induced by MD. Finally, visual and audio-visual processing alterations emerged for the induced component of the neural oscillations, indicating a prominent role of feedback connectivity. Results reveal the causal impact of MD on both unisensory (visual and auditory) and multisensory (audio-visual) processes and, their frequency-specific profiles. These findings support a model in which MD increases excitability to visual events for the deprived eye and audio-visual and auditory input for the non-deprived eye.

To address the issues of poor detection accuracy and the large number of target detection model parameters in existing AGV monocular vision location detection algorithms, this paper presents an AGV vision location method based on Gaussian saliency heuristic. The proposed method introduces a fast and accurate AGV visual detection network called GAGV-net. In the GAGV-net network, a Gaussian saliency feature extraction module is designed to enhance the network’s feature extraction capability, thereby reducing the required output for model fitting. To improve the accuracy of target detection, a joint multi-scale classification and detection task header are designed at the stage of target frame regression to classification. This header utilizes target features of different scales, thereby enhancing the accuracy of target detection. Experimental results demonstrate a 12% improvement in detection accuracy and a 27.38 FPS increase in detection speed compared to existing detection methods. Moreover, the proposed detection network significantly reduces the model’s size, enhances the network model’s deployability on AGVs, and greatly improves detection accuracy.

A robotic assembly system for small components precision assembly is designed, which mainly comprises an industrial robot, three cameras, and a force sensor. The industrial robot is employed to conduct two classic assembly subtasks, i.e., grasping and pose alignment. An automatic grasping method with monocular vision guidance is proposed. The grasping strategy consists of three stages, i.e., aligning, approaching, and grasping, and picks up components with high efficiency. Moreover, a coordinated pose alignment strategy with two eye-to-hand microscopic cameras is developed. It can realize pose alignment efficiently and accurately. Besides, based on differential movement principle, the position offset due to the end-effector’s orientation adjustment is calculated and compensated, which avoids the grasped component out of the microscopic cameras’ field of view. Finally, a series of grasping and pose alignment experiments are conducted on designed robotic precision assembly system to verify the effectiveness of the proposed grasping and pose alignment methods. The grasping success rate is up to 100 % with 30 times experiments, the orientation alignment error is less than 0.05 ∘ , and the position alignment error is less than 26 μ m.