SYSTEM OF PERSON’S VISUAL PERCEPTION AS AN OBJECT OF IMPACT BY LIGHT EMISSION

In modern conditions of hostilities, informativity plays an important role in both defense and offensive operations. Most of the information, including technical, passes through optical systems. Optoelectronic and infrared devices, missile homing heads, the human eye in their structure have optics with different coefficients. A light pulse of different levels makes a negative impact on optical systems decreasing data throughput. One of the possible types of impact on personnel in order to disorient enemy troops and disable optical surveillance and sighting systems is the use of light emission. Light emission can negatively affect the system of visual perception and cause the deterioration of information processing efficiency, so it can be used as a factor for the destruction of enemy personnel. In the process of impact by light emission on the system of visual perception, the phenomenon of after-effect is possible. It consists in the process of adaptation of the system of visual perception to the perception of information after exposure to bright light emission. The visual center of the brain plays a major role in the adaptation processes, so a human can see the transitions of the brightness of the adaptive background. The longest adaptation time occurs when exposed to blue and white light. The increase in the area of receptive fields leads to a decrease in the resolving capabilities of the system of visual perception and affects the effectiveness of the combat task in terms of target recognition and sighting. The main intense light emission impact factors on the enemy’s personnel are: psychological effect (disorientation and distraction) which is manifested in the temporary cessation of task execution, which relates to the unexpected emergence of bright emission; impairment of visual function (when intense bright light emission blocks the system of visual perception; temporary loss of vision; disorientation and epileptic attacks.

Voxel-Based Morphometric Features of Dysthyroid Optic Neuropathy

Background: Dysthyroid optic neuropathy (DON) is a serious complication of thyroid-associated ophthalmology (TAO), leading to loss of vision or blindness. Numerous studies had reported thyroid dysfunction affects a wide range of visual pathways in adults, from the retina to the visual center. We aimed to explore if there were abnormalities of gray matter density (GMD) in DON patients.Methods: We collected patients with TAO from The Eye Hospital of Wenzhou Medical University. All patients underwent routine ophthalmic examination, Clinical Activity Score (CAS), intraocular pressure (IOP), exophthalmos, visual field, OCT,and orbital CT scan. 16 patients with DON and 16 patients without DON (N-DON) were enrolled in this study. Age, gender, orbital congestion index and degree of education of patients were matched between the two groups. High-resolution magnetization-prepared rapid acquisition with gradient echo (MPRAGE) scans was performed on all patients. Voxel-based morphometry (VBM) was applied to analyze the T1 weighted images of the brain, based on functional magnetic resonance imaging (FMRI) integrated VBM (FSL-VBM).Results: GDM was significantly decreased in the right middle temporal gyrus, left middle frontal gyrus, left superior frontal gyrus, and right middle frontal orbicular gyrus in the DON when compared to N-DON.Conclusions: The DON can result in reduced GMD in specific areas of the brain. This finding suggests that there may be other mechanisms in Don

Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks

Research at the intersection of computer vision and neuroscience has revealed hierarchical correspondence between layers of deep convolutional neural networks (DCNNs) and cascade of regions along human ventral visual cortex. Recently, studies have uncovered emergence of human interpretable concepts within DCNNs layers trained to identify visual objects and scenes. Here, we asked whether an artificial neural network (with convolutional structure) trained for visual categorization would demonstrate spatial correspondences with human brain regions showing central/peripheral biases. Using representational similarity analysis, we compared activations of convolutional layers of a DCNN trained for object and scene categorization with neural representations in human brain visual regions. Results reveal a brain-like topographical organization in the layers of the DCNN, such that activations of layer-units with central-bias were associated with brain regions with foveal tendencies (e.g. fusiform gyrus), and activations of layer-units with selectivity for image backgrounds were associated with cortical regions showing peripheral preference (e.g. parahippocampal cortex). The emergence of a categorical topographical correspondence between DCNNs and brain regions suggests these models are a good approximation of the perceptual representation generated by biological neural networks.

Transductive Zero-Shot Learning via Visual Center Adaptation

In this paper, we propose a Visual Center Adaptation Method (VCAM) to address the domain shift problem in zero-shot learning. For the seen classes in the training data, VCAM builds an embedding space by learning the mapping from semantic space to some visual centers. While for unseen classes in the test data, the construction of embedding space is constrained by a symmetric Chamfer-distance term, aiming to adapt the distribution of the synthetic visual centers to that of the real cluster centers. Therefore the learned embedding space can generalize the unseen classes well. Experiments on two widely used datasets demonstrate that our model significantly outperforms state-of-the-art methods.

Visual Function, Organization, and Development of the Mouse Superior Colliculus

The superior colliculus (SC) is the most prominent visual center in mice. Studies over the past decade have greatly advanced our understanding of the function, organization, and development of the mouse SC, which has rapidly become a popular model in vision research. These studies have described the diverse and cell-type-specific visual response properties in the mouse SC, revealed their laminar and topographic organizations, and linked the mouse SC and downstream pathways with visually guided behaviors. Here, we summarize these findings, compare them with the rich literature of SC studies in other species, and highlight important gaps and exciting future directions. Given its clear importance in mouse vision and the available modern neuroscience tools, the mouse SC holds great promise for understanding the cellular, circuit, and developmental mechanisms that underlie visual processing, sensorimotor transformation, and, ultimately, behavior.