Visual Centers Research Articles

A Study on the Design of Embedded Visual Image Teaching Aids to Assist Young Children's Cognitive and Fine Motor Development.

Visual development in infancy is crucial for establishing neural connections and enhancing the growth of the visual center. Adequate visual stimulation supports cognitive learning, helping children integrate images, colors, and shapes. This research examines the design and use of a visual image teaching aid to guide children in exploring image cognition and developing fine motor skills. This study involved 70 kindergarten teachers who participated in a questionnaire survey about 60 children aged from 5 to 6 years old. The results show that visual graphic aids effectively promoted coordination, control, and the integration of visual and fine motor skills in children. Furthermore, these aids supported the development of spatial and environmental relationships through hands-on activities.

Improving rail transit security with enhanced YOLOv5 obstacle detection

Abstract As the economy advances and rail transit technology progresses, rail transit plays an important role in alleviating urban traffic pressure and promoting regional economic development. However, railway safety faces potential hazards such as foreign object intrusion in the trackside environment, seriously threatening the safety of trains and passengers. Therefore, to achieve high-performance recognition of obstacle target images in rail transit, a small sample object detection algorithm YOLOv5-RTO, which using EVC (Explicit Visual Center) attention mechanism and lightweight up-sampling operator CARAFE (Content-Aware ReAssembly of FEatures) to improve YOLOv5, has been proposed. Based on the self-collected image data of rail transit obstacles, a dense and time-varying dataset was created within the rail transit system, and data augmentation methods were used to reduce the impact of small sample datasets on the detection performance of subsequent algorithm. Using this dataset, target detection tests were conducted on rail transit obstacle images to analyze the actual performance of the training model. The experiment results indicate that the enhanced YOLOv5-RTO algorithm integrates the advantages of EVC and CARAFE while preserving the lightweight and accurate characteristics of YOLOv5. The improved algorithm scheme has an average inference time of 1.3 ms per frame in the field of rail transit images, achieving an overall mAP of 96.5%, an increase of 1.8% compared to the original algorithm. It has a certain engineering application value and provides reference for further optimizing the safety system of urban rail transit.

Neuroprotective Effects of Astragalus Polysaccharide on Retina Cells and Ganglion Cell Projection in NMDA-Induced Retinal Injury.

Astragalus polysaccharide (APS), a water-soluble heteropolysaccharide, possesses immunomodulatory, anti-inflammatory, and cardioprotective properties. This study investigates the neuroprotective potential of APS in a model of N-Methyl-d-aspartic acid (NMDA)-induced retinal neurodegeneration, aiming to explore its potential as a treatment for retinal degenerative diseases. Retinal function was evaluated using electroretinography (ERG), optomotor reflex (OMR), and flash visual evoked potentials (FVEP). Retinal inflammatory responses were examined through immunohistochemistry, western blotting (WB), and quantitative reverse transcription PCR (qRT-PCR). To assess the integrity of visual projections, an intravitreal injection of adeno-associated virus (AAV) was employed to trace the projections of retinal ganglion cells (RGCs) to the visual centers. APS treatment conferred protection to retinal cells, as indicated by ERG and OMR assessments. And APS intervention mitigated NMDA-induced apoptosis, evidenced by a decrease in TUNEL-positive cells. Furthermore, APS treatment attenuated the NMDA-induced reduction in RGC projections to the visual centers, including the superior colliculus and lateral geniculate nucleus, as demonstrated by AAV tracing. Our findings reveal that APS shields the retina from NMDA-induced damage by inhibiting the NF-κB signaling pathway and reduces the detrimental effects of NMDA on RGC projections to the visual centers. These findings propose APS as a potential novel therapeutic agent for the treatment of retinal diseases.

A photovoltaic cell defect detection model capable of topological knowledge extraction

As the global transition towards clean energy accelerates, the demand for the widespread adoption of solar energy continues to rise. However, traditional object detection models prove inadequate for handling photovoltaic cell electroluminescence (EL) images, which are characterized by high levels of noise. To address this challenge, we developed an advanced defect detection model specifically designed for photovoltaic cells, which integrates topological knowledge extraction. Our approach begins with the introduction of a multi-scale dynamic context-based feature extraction method, capable of generating static context by thoroughly capturing the local texture and structural information of multi-scale defects. This static context is then combined with dynamic context to produce fine-grained local features. Subsequently, we developed a centralized feature pyramid structure, enhanced by spatial semantics, which models the explicit visual center. This structure effectively elucidates the relationship between local and global features in defect images, thereby improving the representation of defect characteristics. Finally, we implemented a feature enhancement strategy grounded in spatial semantic knowledge extraction. This strategy uncovers potential correlations among defect targets by constructing a spatial semantic topology of features, mapping these features to a higher-order representation, and ultimately delivering precise defect detection results.

Retinal Connectomics: A Review.

The retina is an ideal model for understanding the fundamental rules for how neural networks are constructed. The compact neural networks of the retina perform all of the initial processing of visual information before transmission to higher visual centers in the brain. The field of retinal connectomics uses high-resolution electron microscopy datasets to map the intricate organization of these networks and further our understanding of how these computations are performed by revealing the fundamental topologies and allowable networks behind retinal computations. In this article, we review some of the notable advances that retinal connectomics has provided in our understanding of the specific cells and the organization of their connectivities within the retina, as well as how these are shaped in development and break down in disease. Using these anatomical maps to inform modeling has been, and will continue to be, instrumental in understanding how the retina processes visual signals.

Ten Years of Operating a Center with Large-Scale Virtual-Reality Installations: Developments and Learnings

Abstract Virtual reality (VR) can be found in many fields including art history, education, research, and industry 4.0. Next to lightweight head-mounted displays there are also VR installations, such as immersive 3D screens, large-scale displays, and CAVE systems, that are used in research, scientific visualization, and also the automotive industry. These systems offer high visual quality and collaborative VR experiences for researchers and have long been used in research. We present learnings and insights from ten years of operating and maintaining a visualization center with large-scale immersive displays and installations. Our report aims to answer questions on the benefits and challenges of such a location-based VR center. We broke down the ten years into three phases and discuss the installations themselves, software, and other various developments of the center over time, illustrated with exemplary use cases. Finally, we cover our experiences at the beginning of phase four with the installation of a (novel) LED CAVE and the future (forward-looking) developments we expect for location-based VR centers.

Lightweight transmission line defect identification method based on OFN network and distillation method

AbstractEdge devices are increasingly utilized for defect detection in power line inspection, necessitating algorithms that optimize model size and accuracy. This study introduces a lightweight detection method using the Optimized Feature Network with MobileOne‐FPN‐NASHead (OFN network) OFN network and distillation technique. The OFN incorporates a lightweight backbone, neural network search, re‐parametrization, and a feature pyramid to create a compact yet effective detection network. To enhance feature learning, a heterogeneous distillation approach is applied, leveraging a modified YOLOv8 as a teacher network. This modification includes an explicit visual centre for improved global and local information extraction, crucial for dense target detection in power line inspections. Additionally, the Minimize the points distance IoU (MPDloU) loss function is used to improve localization accuracy over the the Complete‐Intersection Over Union (CIoU) loss. Experimental results show a 1.1% mean Average Precision (mAP) increase for the enhanced YOLOv8 and a 70.2% mAP for the OFN network with 18.95 GFLOPs and 343 FPS, achieving a commendable balance between model efficiency and detection performance. The research underscores the viability of the OFN for edge computing in power line defect detection, highlighting the potential of innovative algorithmic structures in this application.

Optimizing Disaster Response and Mitigation through Digital Communication, Visualization, and Jabar Command Center: A Comprehensive Analysis

Optimizing Disaster Response and Mitigation through Digital Communication, Visualization, and Jabar Command Center: A Comprehensive Analysis

Interim Results of the Phase III Portal Extension Trial of the Port Delivery System with Ranibizumab in Neovascular Age-Related Macular Degeneration

ObjectiveThe Port Delivery System with ranibizumab (PDS) is approved in the United States for neovascular age-related macular degeneration (nAMD). Portal (NCT03683251) is evaluating long-term safety and tolerability of the PDS in patients with nAMD who completed the phase II Ladder (NCT02510794) or phase III Archway (NCT03677934) trials. DesignMulticenter, nonrandomized, open-label, extension clinical trial. ParticipantsAll-PDS safety population (N = 555) comprises patients enrolled in Portal who completed Ladder or Archway. Due to data availability, efficacy population comprises Ladder-to-Portal patients only: patients who previously received PDS 10, 40, or 100 mg/ml pro re nata (as-needed [PRN]; n = 58, 62, 59, respectively) or monthly intravitreal ranibizumab 0.5-mg injections (monthly ranibizumab; n = 41) in Ladder and subsequently enrolled in Portal. MethodsLadder patients received PDS refill-exchanges PRN or monthly ranibizumab. Archway patients received PDS 100 mg/ml with fixed refill-exchanges every 24 weeks (PDS Q24W) or monthly ranibizumab. Once enrolled in Portal, all patients receive PDS Q24W from day 1. Main Outcome MeasuresOcular adverse events of special interest (AESIs); changes from baseline in best-corrected visual acuity (BCVA) and center point thickness (CPT); supplemental ranibizumab treatment between refill-exchange procedures; PDS Patient Preference Questionnaire results. ResultsIn the All-PDS safety population (mean follow-up, 111 weeks), 137 (24.7%) patients had ≥ 1 ocular AESI; most common were cataract (11.4%), vitreous hemorrhage (6.1%), conjunctival thickening (bleb)/filtering bleb leak (6.3%). Endophthalmitis occurred in 11 of 555 (2.0%) patients. For Ladder-to-Portal patients previously treated with PDS 100 mg/ml or monthly ranibizumab, BCVA remained stable from baseline to month 48; mean (95% confidence interval) changes from baseline were 0.1 (−6.6, 6.8; n = 31) and 2.3 (−9.4, 14.1; n = 15) letters, respectively; CPT remained stable through month 48. Approximately 95% of patients did not need supplemental treatment before each refill-exchange for > 2 years since Portal enrollment. Of Ladder-to-Portal previous monthly ranibizumab patients, 92% preferred the PDS over injections. ConclusionsInterim results from Portal suggest 4-year maintenance of visual/anatomic outcomes with PDS 100 mg/ml, with the PDS preferred to monthly injections. Long-term safety profile of the PDS is well characterized.

Label-free photoacoustic computed tomography of visually evoked responses in the primary visual cortex and four subcortical retinorecipient nuclei of anesthetized mice.

Many techniques exist for screening retinal phenotypes in mouse models in vision research, but significant challenges remain for efficiently probing higher visual centers of the brain. Photoacoustic computed tomography (PACT), with optical sensitivity to hemodynamic response (HR) in brain and ultrasound resolution, provides unique advantages in comprehensively assessing higher visual function in the mouse brain. We aim to examine the reliability of PACT in the functional phenotyping of mouse models for vision research. A PACT-ultrasound (US) parallel imaging system was established with a one-dimensional (1D) US transducer array and a tunable laser. Imaging was performed at three coronal planes of the brain, covering the primary visual cortex and the four subcortical nuclei, including the superior colliculus, the dorsal lateral geniculate nucleus, the suprachiasmatic nucleus, and the olivary pretectal nucleus. The visual-evoked HR was isolated from background signals using an impulse-based data processing protocol. rd1 mice with rod/cone degeneration, melanopsin-knockout (mel-KO) mice with photoreceptive ganglion cells that lack intrinsic photosensitivity, and wild-type mice as controls were imaged. The quantitative characteristics of the visual-evoked HR were compared. Quantitative analysis of the HRs shows significant differences among the three mouse strains: (1)rd1 mice showed both smaller and slower responses compared with wild type ( , ) and (2)mel-KO mice had lower amplitude but not significantly delayed photoresponses than wild-type mice ( , ). These results agree with the known visual deficits of the mouse strains. PACT demonstrated sufficient sensitivity to detecting post-retinal functional deficits.

Centralised visual processing center for remote sensing target detection

Target detection in satellite images is an essential topic in the field of remote sensing and computer vision. Despite extensive research efforts, accurate and efficient target detection in remote sensing images remains unsolved due to the large target scale span, dense distribution, and overhead imaging and complex backgrounds, which result in high target feature similarity and serious occlusion. In order to address the above issues in a comprehensive manner, within this paper, we first propose a Centralised Visual Processing Center (CVPC), this structure is a parallel visual processing center for Transformer encoder and CNN, employing a lightweight encoder to capture broad, long-range interdependencies. Pixel-level Learning Center (PLC) module is used to establish pixel-level correlations and improve the depiction of detailed features. CVPC effectively improves the detection efficiency of remote sensing targets with high feature similarity and severe occlusion. Secondly, we propose a centralised feature cross-layer fusion pyramid structure to fuse the results with the CVPC in a top-down manner to enhance the detailed feature representation capability at each layer. Ultimately, we present a Context Enhanced Adaptive Sparse Convolutional Network (CEASC), which improves the accuracy while ensuring the detection efficiency. Based on the above modules, we designed and conducted a series of experiments. These experiments are conducted on three challenging public datasets, DOTA-v1.0, DIOR, and RSDO, showing that our proposed 3CNet achieves a more advanced detection accuracy while balancing the detection speed (78.62% mAP for DOTA-v1.0, 79.12% mAP for DIOR, and 95.50% mAP for RSOD).

Neuronal Stability, Volumetric Changes, and Decrease in GFAP Expression of Marmoset (Callithrix jacchus) Subcortical Visual Nuclei During Aging.

The physiological aging process is well known for functional decline in visual abilities. Among the components of the visual system, the dorsal lateral geniculate nucleus (DLG) and superior colliculus (SC) provide a good model for aging investigations, as these structures constitute the main visual pathways for retinal inputs reaching the visual cortex. However, there are limited data available on quantitative morphological and neurochemical aspects in DLG and SC across lifespan. Here, we used optical density to determine immunoexpression of glial fibrillary acidic protein (GFAP) and design-based stereological probes to estimate the neuronal number, total volume, and layer volume of the DLG and SC in marmosets (Callithrix jacchus), ranging from 36 to 143 months of age. Our results revealed an age-related increase in total volume and layer volume of the DLG, with an overall stability in SC volume. Furthermore, a stable neuronal number was demonstrated in DLG and superficial layers of SC (SCv). A decrease in GFAP immunoexpression was observed in both visual centers. The results indicate region-specific variability in volumetric parameter, possibly attributed to structural plastic events in response to inflammation and compensatory mechanisms at the cellular and subcellular level. Additionally, the DLG and SCv seem to be less vulnerable to aging effects in terms of neuronal number. The neuropeptidergic data suggest that reduced GFAP expression may reflect morphological atrophy in the astroglial cells. This study contributes to updating the current understanding of aging effects in the visual system and stablishes a crucial foundation for future research on visual perception throughout the aging process.

Enhanced Synaptic Inhibition in the Dorsolateral Geniculate Nucleus in a Mouse Model of Glaucoma.

Elevated intraocular pressure (IOP) triggers glaucoma by damaging the output neurons of the retina called retinal ganglion cells (RGCs). This leads to the loss of RGC signaling to visual centers of the brain such as the dorsolateral geniculate nucleus (dLGN), which is critical for processing and relaying information to the cortex for conscious vision. In response to altered levels of activity or synaptic input, neurons can homeostatically modulate postsynaptic neurotransmitter receptor numbers, allowing them to scale their synaptic responses to stabilize spike output. While prior work has indicated unaltered glutamate receptor properties in the glaucomatous dLGN, it is unknown whether glaucoma impacts dLGN inhibition. Here, using DBA/2J mice, which develop elevated IOP beginning at 6-7 months of age, we tested whether the strength of inhibitory synapses on dLGN thalamocortical relay neurons is altered in response to the disease state. We found an enhancement of feedforward disynaptic inhibition arising from local interneurons along with increased amplitude of quantal inhibitory synaptic currents. A combination of immunofluorescence staining for the γ-aminobutyric acid (GABA)A-α1 receptor subunit, peak-scaled nonstationary fluctuation analysis, and measures of homeostatic synaptic scaling pointed to an ∼1.4-fold increase in GABA receptors at postsynaptic inhibitory synapses, although several pieces of evidence indicate a nonuniform scaling across inhibitory synapses within individual relay neurons. Together, these results indicate an increase in inhibitory synaptic strength in the glaucomatous dLGN, potentially pointing toward homeostatic compensation for disruptions in network and neuronal function triggered by increased IOP.

Drosophila medulla neuroblast termination via apoptosis, differentiation, and gliogenic switch is scheduled by the depletion of the neuroepithelial stem cell pool.

The brain is consisted of diverse neurons arising from a limited number of neural stem cells. Drosophila neural stem cells called neuroblasts (NBs) produces specific neural lineages of various lineage sizes depending on their location in the brain. In the Drosophila visual processing centre - the optic lobes (OLs), medulla NBs derived from the neuroepithelium (NE) give rise to neurons and glia cells of the medulla cortex. The timing and the mechanisms responsible for the cessation of medulla NBs are so far not known. In this study, we show that the termination of medulla NBs during early pupal development is determined by the exhaustion of the NE stem cell pool. Hence, altering NE-NB transition during larval neurogenesis disrupts the timely termination of medulla NBs. Medulla NBs terminate neurogenesis via a combination of apoptosis, terminal symmetric division via Prospero, and a switch to gliogenesis via Glial Cell Missing (Gcm); however, these processes occur independently of each other. We also show that temporal progression of the medulla NBs is mostly not required for their termination. As the Drosophila OL shares a similar mode of division with mammalian neurogenesis, understanding when and how these progenitors cease proliferation during development can have important implications for mammalian brain size determination and regulation of its overall function.

ЭСТЕТИЧЕСКИЕ МОДЕЛИ ВХОЖДЕНИЯ ВОССТАНАВЛИВАЕМЫХ ПРОМЫШЛЕННЫХ ОБЪЕКТОВ В СТРУКТУРУ ОКРУЖАЮЩЕЙ ЗАСТРОЙКИ

The article presents models of aesthetic formation of reconstructed industrial objects of the Soviet period in the surrounding development in the context of sustainability at the level of the city structure, taking into account the growth of the urban fabric. The industrial enterprises in question, as a rule, initially located on the outskirts of the city. Due to the dynamics of urbanization processes, they have become tightly integrated into the urban fabric, and are currently so-called exclusion zones, since they are large-sized relative to the surrounding development objects, including residential buildings and elements of the infrastructure of its social and domestic services. It is also necessary to note the enterprises that are city-forming and located in the structure of the historical center of the urban settlement, where it is especially important to preserve the authenticity of the historical development: to introduce reconstructed industrial facilities as delicately as possible (to select the aesthetic model of the reconstructed enterprise) into the historically established visual and aesthetic code of the city. Thus, two models of aesthetic inclusion of existing industrial enterprises being restored into the city structure have been identified: mimicry, related to objects located in the historical part of the city, and accentual – forming a visual center of attraction outside the historical part of the city, on the periphery and outside the city. The article also examines options for aesthetic inclusion of industrial objects being reconstructed depending on their urban development features: the presence of buffer zones and sanitary protection zones, the presence of external railway routes adjacent to the objects

DEW-YOLO: An Efficient Algorithm for Steel Surface Defect Detection

To address the current steel surface defect detection algorithms in practical applications involving low detection accuracy, an efficient and highly accurate strip steel surface defect detection algorithm, DEW-YOLO, is proposed in this paper. Firstly, by combining the advantages of deformable convolutional networks (DCNs), this paper innovates the C2F module in YOLOv8 and proposes a C2f_DCN module that can flexibly sample features to enhance the abilities of learning and expressing defect features of different sizes and shapes. Secondly, the explicit visual center (EVC) is introduced into the backbone network, which enhances feature extraction capabilities and adaptability and enables the model to better adjust features at different levels and scales. Finally, the original loss function is replaced with the Wise-IoU (WIoU) loss function to accurately measure the similarity between the target frames and improve the defect detection performance of the model. The experimental results on the NEU-DET dataset demonstrate that the algorithms proposed in this paper achieved a mean average precision (mAP) of 80.3% in steel surface defect detection tasks, which was a 3.9% improvement over the original YOLOv8 model. The model’s inference speed reached 91 frames per second (FPS). DEW-YOLO effectively enhances the accuracy of steel defect detection and better satisfies industrial inspection requirements.

HLG-YOLOv7: small object detection in conveyor belt damage based on leveraging hybrid local and global features

In the industrial production process, the detection of conveyor belt damage plays a crucial role in ensuring the stable operation of the transportation system. To tackle the issues of significant changes in damage size, missed detections, and poor detection ability of small-size objects in conveyor belt surface damage detection, an improved HLG-YOLOv7 (Hybrid Local and Global Features Network) conveyor belt surface defect detection algorithm is proposed. Firstly, Next-VIT is employed as the backbone network to fully extract local and global features of the damage, enhancing the model’s ability to extract features of different-sized damages. Additionally, to deeply utilize the extracted local and global features, the Explicit Visual Center (EVC) feature fusion module is introduced to obtain comprehensive and discriminative feature representations, further enhancing the detection capability of small objects. Lastly, a lightweight neck structure is designed using GSConv to reduce the complexity of the model. Experimental results demonstrate that the proposed method has better small object detection performance compared to existing methods. The improved algorithm achieves mAP and F1 scores of 96.24% and 97.15%, respectively, with an FPS of 28.2.

Noninvasive imaging-guided ultrasonic neurostimulation with arbitrary 2D patterns and its application for high-quality vision restoration.

Retinal degeneration, a leading cause of irreversible low vision and blindness globally, can be partially addressed by retina prostheses which stimulate remaining neurons in the retina. However, existing electrode-based treatments are invasive, posing substantial risks to patients and healthcare providers. Here, we introduce a completely noninvasive ultrasonic retina prosthesis, featuring a customized ultrasound two-dimensional array which allows for simultaneous imaging and stimulation. With synchronous three-dimensional imaging guidance and auto-alignment technology, ultrasonic retina prosthesis can generate programmed ultrasound waves to dynamically and precisely form arbitrary wave patterns on the retina. Neuron responses in the brain's visual center mirrored these patterns, evidencing successful artificial vision creation, which was further corroborated in behavior experiments. Quantitative analysis of the spatial-temporal resolution and field of view demonstrated advanced performance of ultrasonic retina prosthesis and elucidated the biophysical mechanism of retinal stimulation. As a noninvasive blindness prosthesis, ultrasonic retina prosthesis could lead to a more effective, widely acceptable treatment for blind patients. Its real-time imaging-guided stimulation strategy with a single ultrasound array, could also benefit ultrasound neurostimulation in other diseases.

YOLOv5s-CEDB: A robust and efficiency Camellia oleifera fruit detection algorithm in complex natural scenes

To solve the problem of poor recognition accuracy caused by various colors, uneven distribution, and occlusion by branches and leaves of Camellia oleifera fruits under natural growth conditions, this study proposes an improved deep learning network, YOLOv5s-CEDB, for Camellia oleifera fruit detection based on YOLOv5s. Coordinate Attention Mechanism (CoordAtt), Deformable Convolution (DConv), and Explicit Visual Center (EVC) are introduced to enhance the network’s local and global feature extraction performance. To improve the detection performance for small and dense targets, the feature fusion module of the network was replaced with the designed light-bidirectional feature pyramid (light-BiFPN) structure. GhostConv was used to reduce the parameters and inference speed of the structure. A dataset with different light conditions, colors, and density levels of Camellia oleifera fruits was established, and performance evaluation experiments were conducted. Experimental results showed that the mean Average Precision (mAP) and F1-score of the designed YOLOv5s-CEDB network reached 91.4 % and 89.6 %, respectively, which were 2.6 % and 3.6 % higher than those of the original YOLOv5s model, respectively, and the influencing frame rate arrived at 37.6 FPS. Under different colors, distribution densities, occlusion scenarios, and light intensities, the detection accuracy of the YOLOv5s-CEDB network model was significantly better than those of the YOLOv5s, YOLOv8s and Faster-RCNN networks. It was verified that the proposed YOLOv5s-CEDB network could significantly improve the accuracy and stability of Camellia oleifera fruit detection, satisfying the requirements of yield estimation and intelligent harvesting.

Transformation of Motion Pattern Selectivity from Retina to Superior Colliculus.

The superior colliculus (SC) is a prominent and conserved visual center in all vertebrates. In mice, the most superficial lamina of the SC is enriched with neurons that are selective for the moving direction of visual stimuli. Here, we study how these direction selective neurons respond to complex motion patterns known as plaids, using two-photon calcium imaging in awake male and female mice. The plaid pattern consists of two superimposed sinusoidal gratings moving in different directions, giving an apparent pattern direction that lies between the directions of the two component gratings. Most direction selective neurons in the mouse SC respond robustly to the plaids and show a high selectivity for the moving direction of the plaid pattern but not of its components. Pattern motion selectivity is seen in both excitatory and inhibitory SC neurons and is especially prevalent in response to plaids with large cross angles between the two component gratings. However, retinal inputs to the SC are ambiguous in their selectivity to pattern versus component motion. Modeling suggests that pattern motion selectivity in the SC can arise from a nonlinear transformation of converging retinal inputs. In contrast, the prevalence of pattern motion selective neurons is not seen in the primary visual cortex (V1). These results demonstrate an interesting difference between the SC and V1 in motion processing and reveal the SC as an important site for encoding pattern motion.