Collaborating Authors

Commercial Services & Supplies

Retinal waves prime visual motion detection by simulating future optic flow


As a mouse runs forward across the forest floor, the scenery that it passes flows backwards. Ge et al. show that the developing mouse retina practices in advance for what the eyes must later process as the mouse moves. Spontaneous waves of retinal activity flow in the same pattern as would be produced days later by actual movement through the environment. This patterned, spontaneous activity refines the responsiveness of cells in the brain's superior colliculus, which receives neural signals from the retina to process directional information. Science , abd0830, this issue p. [eabd0830][1] ### INTRODUCTION Fundamental circuit features of the mouse visual system emerge before the onset of vision, allowing the mouse to perceive objects and detect visual motion immediately upon eye opening. How the mouse visual system achieves self-organization by the time of eye opening without structured external sensory input is not well understood. In the absence of sensory drive, the developing retina generates spontaneous activity in the form of propagating waves. Past work has shown that spontaneous retinal waves provide the correlated activity necessary to refine the development of gross topographic maps in downstream visual areas, such as retinotopy and eye-specific segregation, but it is unclear whether waves also convey information that instructs the development of higher-order visual response properties, such as direction selectivity, at eye opening. ### RATIONALE Spontaneous retinal waves exhibit stereotyped changing spatiotemporal patterns throughout development. To characterize the spatiotemporal properties of waves during development, we used one-photon wide-field calcium imaging of retinal axons projecting to the superior colliculus in awake neonatal mice. We identified a consistent propagation bias that occurred during a transient developmental window shortly before eye opening. Using quantitative analysis, we investigated whether the directionally biased retinal waves conveyed ethological information relevant to future visual inputs. To understand the origin of directional retinal waves, we used pharmacological, optogenetic, and genetic strategies to identify the retinal circuitry underlying the propagation bias. Finally, to evaluate the role of directional retinal waves in visual system development, we used pharmacological and genetic strategies to chronically manipulate wave directionality and used two-photon calcium imaging to measure responses to visual motion in the midbrain superior colliculus immediately after eye opening. ### RESULTS We found that spontaneous retinal waves in mice exhibit a distinct propagation bias in the temporal-to-nasal direction during a transient window of development (postnatal day 8 to day 11). The spatial geometry of directional wave flow aligns strongly with the optic flow pattern generated by forward self-motion, a dominant natural optic flow pattern after eye opening. We identified an intrinsic asymmetry in the retinal circuit that enforced the wave propagation bias involving the same circuit elements necessary for motion detection in the adult retina, specifically asymmetric inhibition from starburst amacrine cells through γ-aminobutyric acid type A (GABAA) receptors. Finally, manipulation of directional retinal waves, through either the chronic delivery of gabazine to block GABAergic inhibition or the starburst amacrine cell–specific mutation of the FRMD7 gene, impaired the development of responses to visual motion in superior colliculus neurons downstream of the retina. ### CONCLUSION Our results show that spontaneous activity in the developing retina prior to vision onset is structured to convey essential information for the development of visual response properties before the onset of visual experience. Spontaneous retinal waves simulate future optic flow patterns produced by forward motion through space, due to an asymmetric retinal circuit that has an evolutionarily conserved link with motion detection circuitry in the mature retina. Furthermore, the ethologically relevant information relayed by directional retinal waves enhances the development of higher-order visual function in the downstream visual system prior to eye opening. These findings provide insight into the activity-dependent mechanisms that regulate the self-organization of brain circuits before sensory experience begins. ![Figure][2] Origin and function of directional retinal waves. ( A ) Imaging of retinal axon activity reveals a propagation bias in spontaneous retinal waves (scale bar, 500 μm). ( B ) Cartoon depiction of wave flow vectors projected onto visual space. Vectors (black arrows) align with the optic flow pattern (red arrows) generated by forward self-motion. ( C ) Asymmetric GABAergic inhibition in the retina mediates wave directionality. ( D ) Developmental manipulation of wave directionality disrupts direction-selective responses in downstream superior colliculus neurons at eye opening. The ability to perceive and respond to environmental stimuli emerges in the absence of sensory experience. Spontaneous retinal activity prior to eye opening guides the refinement of retinotopy and eye-specific segregation in mammals, but its role in the development of higher-order visual response properties remains unclear. Here, we describe a transient window in neonatal mouse development during which the spatial propagation of spontaneous retinal waves resembles the optic flow pattern generated by forward self-motion. We show that wave directionality requires the same circuit components that form the adult direction-selective retinal circuit and that chronic disruption of wave directionality alters the development of direction-selective responses of superior colliculus neurons. These data demonstrate how the developing visual system patterns spontaneous activity to simulate ethologically relevant features of the external world and thereby instruct self-organization. [1]: /lookup/doi/10.1126/science.abd0830 [2]: pending:yes

A security practitioner's roadmap to artificial intelligence


Artificial Intelligence applies algorithms to leverage deep learning and other techniques to solve actual problems. The sub-field of Vision Intelligence impacts the physical security industry directly when you consider that surveillance cameras are the ultimate end-point device, the "all-seeing eyes" of the Internet. Could "intelligence" be applied to video to create "human context" to eliminate the false alarms and tailgating pain points that have long plagued the physical security industry? As a security leader, Philip Jang, Sr. Manager Physical Systems & Technology at VMWare, has been exploring how AI technologies and digital transformation processes impact the physical security profession for several years. Jang has global enterprise experience in the planning, execution, delivery and automation of advanced technologies such as AI, Robotics, Drones, advanced analytics and IoT.

Security researchers fool Microsoft's Windows Hello authentication system


Microsoft designed Windows Hello to be compatible with webcams across multiple brands, but that feature designed for ease of adoption could also make the technology vulnerable to bad actors. As reported by Wired, researchers from the security firm CyberArk managed to fool the Hello facial recognition system using images of the computer owner's face. Windows Hello requires the use of cameras with both RGB and infrared sensors, but upon investigating the authentication system, the researchers found that it only processes infrared frames. To verify their finding, the researchers created a custom USB device, which they loaded with infrared photos of the user and RGB images of Spongebob. Hello recognized the device as a USB camera, and it was successfully unlocked with just the IR photos of the user.

Ring Video Doorbell 4 review: Great for people deep in the Ring ecosystem; just good for everyone else


Ring now offers seven video doorbell models, and as you might have guessed, the company is running out of ways to differentiate them. The Ring Video Doorbell 4 looks virtually identical to the Ring Video Doorbell 3 (and the battery-only Ring Video Doorbell 2, for that matter), and it delivers the same 1080p resolution. Like the model 3, the Ring Video Doorbell 4 can operate on either battery power or your existing doorbell wiring, and both models support dual-band Wi-Fi networks (2.4- and 5GHz). That leaves color pre-roll video previews (more on that in a bit) as the only additional feature you'll get for the extra $20 in cost. As is typical of Ring home-security products, you'll need to sign up for a subscription to unlock all the Ring Video Doorbell 4's capabilities.

How facial recognition solutions can safeguard the hybrid workplace - Help Net Security


The number of US adults teleworking due to the pandemic fell by 30% between January and May 2021 (from 23% to 16%), with the biggest drop in May. As more employees return to their offices, new and unexpected challenges hidden within the new hybrid work model threaten to severely disrupt safety and security. Another added complication is that newly relaxed CDC (Centers for Disease Control) guidelines do not apply to unvaccinated people or account for variations in local or state health guidelines that may be in place, potentially creating a need for organizations to identify and track different groups of employees and visitors. Thankfully, AI-based solutions exist to bolster the security of in-person workplaces and enhance key elements, from the sign-in process to restricted area enforcement, while also allowing for frictionless adherence to health guidelines. As the most accurate, turnkey biometric solution, facial recognition has the potential to solve many of the looming challenges offices will soon face as employees return to work.

How AI is helping enterprises turn the tables on malicious attacks


Malicious attackers have turned to AI to invade enterprise networks. To combat attacks, organizations need to embrace AI in turn. Join this VB Live event to learn more about the powerful, proactive AI security solutions that are enabling intelligent threat detection and response, security operations and maintenance, and more. Check off another consequence of COVID: It's directly responsible for the uptick in security risks for organizations. Many companies were forced to accelerate digital transformation, adopting brand-new technologies and policies to meet pandemic challenges.

How Machine Learning Can Improve Your Debt Collection Process -- Lateral


Developments in machine learning (ML) and Artificial Intelligence (AI) are having a great impact on the debt collection industry. At its core Machine Learning generates predictive models using algorithms that learn from data. The idea is that if we can input enough useful and reliable data, we can build models which can make predictions on our behalf. There are a number of ways in which machine learning can aid and improve the debt collection process: Reduce Workloads Collections departments place calls, send countless emails, and seek to work out payment plans — and very frequently none of the above activities translate into the successful recovery of debt. With ML this changes. Since tasks are automated, users experience higher productivity and less time spent on labour-intensive tasks. Protecting Your Business Reputation Since ML can automate communication, you know that all your business correspondence will be professional, methodical and unambiguous. LATERAL’S debt collections software provides its users with a non-intrusive, customer-driven point of engagement, which is proven to be highly successful.  

Detection of abnormal events in videos


The rapid advancements in the technology of closed circuit television cameras and their underlying infrastructure has led to a sheer number of surveillance cameras being implemented globally, estimated to go beyond 1 billion by the end of the year 2021 . Considering the massive amounts of videos generated in real-time, manual video analysis by human operator becomes inefficient, expensive, and nearly impossible, which in turn makes a great demand for automated and intelligent methods for an efficient video surveillance system. An important task in video surveillance is anomaly detection, which refers to the identification of events that do not conform to the expected behavior. Abnormal events in the general sense have the characteristics of suddenness,in order to be able to understand the abnormal events in the first time, it usually takes a lot of manpower to stare at the monitoring screen for a long time to observe, so It will not only make people tired, but also easily overlook some inconspicuous events. Therefore, the automatic detection and recognition of abnormal events of surveillance video in complex scenes, as the core subject of intelligent video surveillance systems, is receiving more and more attention from researchers.

Breezing Through Airport Security- AI/ML Assisted Self Screening


Imagine breezing through airport security checkpoints without interacting with TSA officers. TSA may be thinking that the feeling is mutual! In this article, I write about a program known as…

COMSovereign to Acquire RVision, Inc., Expanding Smart City Capabilities


COMSovereign Holding Corp. (NASDAQ: COMS) ("COMSovereign" or "Company"), a U.S.-based developer of 4G LTE Advanced and 5G Communication Systems and Solutions, today announced that it has executed an agreement to acquire RVision, Inc. ("RVision"), a developer of technologically advanced, environmentally hardened video and communications products and physical security solutions designed for government and private sector commercial industries. Terms of the transaction include total consideration of approximately $5.58 million consisting exclusively of shares of restricted common stock. The transaction is expected to close within approximately 15 days subject to traditional closing conditions. Smart Cities and Smart Campuses (educational and industrial) are urban areas designed to integrate advanced technologies including IoT ("Internet of Things"), AI ("Artificial Intelligence"), machine learning, Big Data, and sustainable or "green" energy systems to benefit and secure the daily lives of its residents. Around the world today, these technologies are being deployed to efficiently improve public services and safety through enhancements to everything from mass transportation and waste management to the real-time monitoring of environmental conditions including air and water quality.