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Abstract Implantable image sensors have the potential to revolutionize neuroscience. Due to their small form factor requirements; however, conventional filters and optics cannot be implemented. These limitations obstruct high-resolution imaging of large neural densities. Recent advances in angle-sensitive image sensors and single-photon avalanche diodes have provided a path toward ultrathin lens-less fluorescence imaging, enabling plenoptic sensing by extending sensing capabilities to include photon arrival time and incident angle, thereby providing the opportunity for separability of fluorescence point sources within the context of light-field microscopy (LFM). However, the addition of spectral sensitivity to angle-sensitive LFM reduces imager resolution because each wavelength requires a separate pixel subset. Here, we present a 1024-pixel, 50  µm thick implantable shank-based neural imager with color-filter-grating-based angle-sensitive pixels. This angular-spectral sensitive front end combines a metal–insulator–metal (MIM) Fabry–Perot color filter and diffractive optics to produce the measurement of orthogonal light-field information from two distinct colors within a single photodetector. The result is the ability to add independent color sensing to LFM while doubling the effective pixel density. The implantable imager combines angular-spectral and temporal information to demix and localize multispectral fluorescent targets. In this initial prototype, this is demonstrated with 45 μm diameter fluorescently labeled beads in scattering medium. Fluorescent lifetime imaging is exploited to further aid source separation, in addition to detecting pH through lifetime changes in fluorescent dyes. While these initial fluorescent targets are considerably brighter than fluorescently labeled neurons, further improvements will allow the application of these techniques to in-vivo multifluorescent structural and functional neural imaging. 

Abstract Direct deposition of organic light‐emitting diodes (OLEDs) on silicon‐based complementary metal–oxide–semiconductor (CMOS) chips has enabled self‐emissive microdisplays with high resolution and fill‐factor. Emerging applications of OLEDs in augmented and virtual reality (AR/VR) displays and in biomedical applications, e.g., as brain implants for cell‐specific light delivery in optogenetics, require light intensities orders of magnitude above those found in traditional displays. Further requirements often include a microscopic device footprint, a specific shape and ultrastable passivation, e.g., to ensure biocompatibility and minimal invasiveness of OLED‐based implants. In this work, up to 1024 ultrabright, microscopic OLEDs are deposited directly on needle‐shaped CMOS chips. Transmission electron microscopy and energy‐dispersive X‐ray spectroscopy are performed on the foundry‐provided aluminum contact pads of the CMOS chips to guide a systematic optimization of the contacts. Plasma treatment and implementation of silver interlayers lead to ohmic contact conditions and thus facilitate direct vacuum deposition of orange‐ and blue‐emitting OLED stacks leading to micrometer‐sized pixels on the chips. The electronics in each needle allow each pixel to switch individually. The OLED pixels generate a mean optical power density of 0.25 mW mm −2 , corresponding to >40 000 cd m −2 , well above the requirement for daylight AR applications and optogenetic single‐unit activation in the brain. 

Abstract Coronavirus disease (COVID-19) is a contagious respiratory disease caused by the SARS-CoV-2 virus. The clinical phenotypes are variable, ranging from spontaneous recovery to serious illness and death. On March 2020, a global COVID-19 pandemic was declared by the World Health Organization (WHO). As of February 2023, almost 670 million cases and 6,8 million deaths have been confirmed worldwide. Coronaviruses, including SARS-CoV-2, contain a single-stranded RNA genome enclosed in a viral capsid consisting of four structural proteins: the nucleocapsid (N) protein, in the ribonucleoprotein core, the spike (S) protein, the envelope (E) protein, and the membrane (M) protein, embedded in the surface envelope. In particular, the E protein is a poorly characterized viroporin with high identity amongst all the β-coronaviruses (SARS-CoV-2, SARS-CoV, MERS-CoV, HCoV-OC43) and a low mutation rate. Here, we focused our attention on the study of SARS-CoV-2 E and M proteins, and we found a general perturbation of the host cell calcium (Ca 2+ ) homeostasis and a selective rearrangement of the interorganelle contact sites. In vitro and in vivo biochemical analyses revealed that the binding of specific nanobodies to soluble regions of SARS-CoV-2 E protein reversed the observed phenotypes, suggesting that the E protein might be an important therapeutic candidate not only for vaccine development, but also for the clinical management of COVID designing drug regimens that, so far, are very limited. 

Abstract The use of optogenetic stimulation to evoke neuronal activity in targeted neural populations—enabled by opsins with fast kinetics, high sensitivity and cell-type and subcellular specificity—is a powerful tool in neuroscience. However, to interface with the opsins, deep-brain light delivery systems are required that match the scale of the spatial and temporal control offered by the molecular actuators. Here we show that organic light-emitting diodes can be combined with complementary metal–oxide–semiconductor technology to create bright, actively multiplexed emissive elements. We create implantable shanks in which 1,024 individually addressable organic light-emitting diode pixels with a 24.5 µm pitch are integrated with active complementary metal–oxide–semiconductor drive and control circuitry. This integration is enabled by controlled electrode conditioning, monolithic deposition of the organic light-emitting diodes and optimized thin-film encapsulation. The resulting probes can be used to access brain regions as deep as 5 mm and selectively activate individual neurons with millisecond-level precision in mice. 

Large language models have substantially advanced nuance and context understanding in natural language processing (NLP), further fueling the growth of intelligent conversational interfaces and virtual assistants. However, their hefty computational and memory demands make them potentially expensive to deploy on cloudless edge platforms with strict latency and energy requirements. For example, an inference pass using the state-of-the-art BERT-base model must serially traverse through 12 computationally intensive transformer layers, each layer containing 12 parallel attention heads whose outputs concatenate to drive a large feed-forward network. To reduce computation latency, several algorithmic optimizations have been proposed, e.g., a recent algorithm dynamically matches linguistic complexity with model sizes via entropy-based early exit. Deploying such transformer models on edge platforms requires careful co-design and optimizations from algorithms to circuits, where energy consumption is a key design consideration. 

Graphene nanogap devices emit light due to oxygen in the gap while showing strong negative differential resistance. 

Theoretical and experimental studies of electron-hole friction limited transport in bilayer graphene with a tunable bandgap.