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The brain-machine interface race is on. While Elon Musk’s Neuralink has garnered most of the headlines in this field, a new small and thin chip out of Switzerland makes it look downright clunky by comparison. It also works impressively well.

The chip has been developed by researchers at the Ecole Polytechnique Federale de Lausanne (EPFL) and represents a leap forward in the sizzling space of brain-machine-interfaces (BMIs) – devices that are able to read activity in the brain and translate it into real-world output such as text on a screen. That’s because this particular device – known as a miniaturized brain-machine interface (MiBMI) – is extremely small, consisting of two thin chips measuring just 8 mm² total. By comparison, Elon Musk’s Neuralink device clocks in at comparatively gargantuan size of about 23 × 8 mm (about 0.3 x .9 in).

Additionally, the EPFL chipset uses very little power, is reported to be minimally invasive, and consists of a fully integrated system that processes data in real time. That’s different from Neuralink, which requires the insertion of 64 electrodes into the brain and carries out its processing via an app located on a device outside of the brain.

Ghost imaging in the time domain allows for reconstructing fast temporal objects using a slow photodetector. The technique involves correlating random or pre-programmed probing temporal intensity patterns with the integrated signal measured after modulation by the temporal object. However, the implementation of temporal ghost imaging necessitates ultrafast detectors or modulators for measuring or pre-programming the probing intensity patterns, which are not available in all spectral regions especially in the mid-infrared region.

Brain-machine interfaces (BMIs) have emerged as a promising solution for restoring communication and control to individuals with severe motor impairments. Traditionally, these systems have been bulky, power-intensive, and limited in their practical applications. Researchers at EPFL have developed the first high-performance, Miniaturized Brain-Machine Interface (MiBMI), offering an extremely small, low-power, highly accurate, and versatile solution.

Published in the latest issue of the IEEE Journal of Solid-State Circuits (“MiBMI: A 192/512-Channel 2.46mm 2 Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes”) and presented at the International Solid-State Circuits Conference, the MiBMI not only enhances the efficiency and scalability of brain-machine interfaces but also paves the way for practical, fully implantable devices. This technology holds the potential to significantly improve the quality of life for patients with conditions such as amyotrophic lateral sclerosis (ALS) and spinal cord injuries.

An image of the chip. (Image: EPFL)

Noninvasive braincomputer interfaces could vastly improve brain computer control.

Over the past two decades, the international biomedical research community has demonstrated increasingly sophisticated ways to allow a person’s brain to communicate with a device, allowing breakthroughs aimed at improving quality of life, such as access to computers and the internet, and more recently control of a prosthetic limb. DARPA has been at the forefront of this research.

The state of the art in brain-system communications has employed invasive techniques that allow precise, high-quality connections to specific neurons or groups of neurons. These techniques have helped patients with brain injury and other illnesses. However, these techniques are not appropriate for able-bodied people. DARPA now seeks to achieve high levels of brain-system communications without surgery, in its new program, Next-Generation Nonsurgical Neurotechnology (N³).

Continue reading “Nonsurgical Neural Interfaces Could Significantly Expand Use of Neurotechnology” »

Recently, two-dimensional (2D) materials have gained immense attention, as they are promising in various application fields, such as energy storage, thermal management, photodetectors, catalysis, field-effect transistors, and photovoltaic modules. These merits of 2D materials are attributed to their unique structure and properties. Chirality is an intrinsic property of a substance, which means the substance can not overlap with its mirror image. Significant progress has been made in chiral science, for chirality uniquely influences a chiral substance’s performance. With the rapid development of chiral science, it became unveiled that chirality not only exists in chiral organic molecules but can also be induced in 2D inorganic materials and 2D organic-inorganic hybrid materials by breaking the chiral symmetry within their framework to form 2D chiral materials. Compared with 2D materials that do not have chirality, these 2D inorganic chiral materials and 2D organic-inorganic hybrid chiral materials exhibit innovative performance due to chiral symmetry breaking. Nevertheless, at present, only a fraction of work is available which comprehensively sums up the progress of these promising 2D chiral materials. Thus, given their high potential, it is urgent to summarize these newly developed 2D chiral materials comprehensively. In the current study, to feature and highlight their major significance, the recent progress of 2D inorganic materials and 2D organic-inorganic hybrid materials from their chemical composition and categories, application potential associated with their unique properties, and present synthesis strategies to fabricate them along with discussion concerning the development challenges and their bright future were reviewed. This review is anticipated to be instructive and provide a high understanding of advanced functional 2D materials with chirality.

Keywords: Chirality, two-dimensional, inorganic, organic-inorganic hybrid, asymmetric, enantioselective, chiral-induced spin selectivity (CISS), photoelectronic, spintronics.

This experiment offers new insights into quantum mechanics, simulating how an electron might spontaneously move backward in time.

Here, the authors present a data storage and computation engine comprised of DNA adsorbed to soft dendricolloids, demonstrating end-to-end capabilities from archival storage to non-destructive file access for reading, erasing, rewriting and computing.

Dynamic nuclear polarization (DNP) has revolutionized the field of nanoscale nuclear magnetic resonance (NMR), making it possible to study a wider range of materials, biomolecules and complex dynamic processes such as how proteins fold and change shape inside a cell.

A team of researchers at the University of Waterloo are combining pulsed DNP with nanoscale magnetic resonance force microscopy (MRFM) measurements to demonstrate that this process can be implemented on the nanoscale with high efficiency. The effort is overseen by Dr. Raffi Budakian, faculty member of the Institute for Quantum Computing and a professor in the Department of Physics and Astronomy, and his team consisting of Sahand Tabatabaei, Pritam Priyadarshi, Namanish Singh, Pardis Sahafi, and Dr. Daniel Tay.

The research has been published in Science Advances (“Large-Enhancement Nanoscale Dynamic Nuclear Polarization Near a Silicon Nanowire Surface”).

Researchers from North Carolina State University and Johns Hopkins University have demonstrated a technology capable of a suite of data storage and computing functions – repeatedly storing, retrieving, computing, erasing or rewriting data – that uses DNA rather than conventional electronics. Previous DNA data storage and computing technologies could complete some but not all of these tasks.

“In conventional computing technologies, we take for granted that the ways data are stored and the way data are processed are compatible with each other,” says project leader Albert Keung, co-corresponding author of a paper on the work (Nature Nanotechnology, “A Primordial DNA Store and Compute Engine”). “But in reality, data storage and data processing are done in separate parts of the computer, and modern computers are a network of complex technologies,” Keung is an associate professor of chemical and biomolecular engineering and a Goodnight Distinguished Scholar at NC State.

“DNA computing has been grappling with the challenge of how to store, retrieve and compute when the data is being stored in the form of nucleic acids,” Keung says. “For electronic computing, the fact that all of a device’s components are compatible is one reason those technologies are attractive. But, to date, it’s been thought that while DNA data storage may be useful for long-term data storage, it would be difficult or impossible to develop a DNA technology that encompassed the full range of operations found in traditional electronic devices: storing and moving data; the ability to read, erase, rewrite, reload or compute specific data files; and doing all of these things in programmable and repeatable ways.