Have you ever wonder how SNNs work and their difference from traditional neural networks? Or how SNNs play an important role in computing beyond the Moore’s Law?
What is SNN?
Spiking neural network (SNN) is a new form of neural networks with biologically realistic mechanisms designed to emulate the efficiency and effectiveness of the biological brain.
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.
“DNA, RNA and proteins are the key players to regulate all processes in the cells of our body,” Leiden Professor John van Noort explains. “To understand the (mis-)functioning of these molecules, it is essential to uncover how their 3D structure depends on their sequence and for this it is necessary to measure them one molecule at a time. However, single-molecule measurements are laborious and slow, and the number of possible sequence variations is massive.”
Now the team of scientists developed an innovative tool, called SPARXS (Single-molecule Parallel Analysis for Rapid eXploration of Sequence space), that allows for studying millions of DNA molecules simultaneously.
Continue reading “Revealing DNA behavior in record time (w/video)” »
CRISPR-Cas systems, defense systems in bacteria, have become a plentiful source of technologies for molecular diagnostics. Researchers at the Helmholtz Institute for RNA-based Infection Research (HIRI) in Würzburg have expanded this extensive toolbox. Their novel method, called PUMA, enables the detection of RNA with Cas12 nucleases, which naturally target DNA. PUMA promises a wide range of applications and high accuracy.
The team published its results in the journal Nature Communications (“TracrRNA reprogramming enables direct PAM-independent detection of RNA with diverse DNA-targeting Cas12 nucleases”).
Bacteria have developed special defense mechanisms to protect themselves against viruses, which by no means infect only humans. As part of these so-called CRISPR-Cas systems, a CRISPR ribonucleic acid (crRNA), which serves as a “guide RNA,” recognizes regions of a foreign genome, such as viral DNA. The CRISPR-associated (Cas) nuclease, directed by a crRNA, then renders it harmless by cutting it like a pair of scissors.
Researchers worldwide can now create highly realistic brain cortical organoids — essentially miniature artificial brains with functioning neural networks — thanks to a proprietary protocol released this month by researchers at the University of California San Diego.
The new technique, published in Nature Protocols (“Generation of ‘semi-guided’ cortical organoids with complex neural oscillations”), paves the way for scientists to perform more advanced research regarding autism, schizophrenia and other neurological disorders in which the brain’s structure is usually typical, but electrical activity is altered. That’s according to Alysson Muotri, Ph.D., corresponding author and director of the UC San Diego Sanford Stem Cell Institute (SSCI) Integrated Space Stem Cell Orbital Research Center. The SSCI is directed by Dr. Catriona Jamieson, a leading physician-scientist in cancer stem cell biology whose research explores the fundamental question of how space alters cancer progression.
The newly detailed method allows for the creation of tiny replicas of the human brain so realistic that they rival “the complexity of the fetal brain’s neural network,” according to Muotri, who is also a professor in the UC San Diego School of Medicine’s Departments of Pediatrics and Cellular and Molecular Medicine. His brain replicas have already traveled to the International Space Station (ISS), where their activity was studied under conditions of microgravity.
In a study published in Cell Reports Physical Science (“Electro-Active Polymer Hydrogels Exhibit Emergent Memory When Embodied in a Simulated Game-Environment”), a team led by Dr Yoshikatsu Hayashi demonstrated that a simple hydrogel — a type of soft, flexible material — can learn to play the simple 1970s computer game ‘Pong’. The hydrogel, interfaced with a computer simulation of the classic game via a custom-built multi-electrode array, showed improved performance over time.
Dr Hayashi, a biomedical engineer at the University of Reading’s School of Biological Sciences, said: Our research shows that even very simple materials can exhibit complex, adaptive behaviours typically associated with living systems or sophisticated AI.
This opens up exciting possibilities for developing new types of ‘smart’ materials that can learn and adapt to their environment.
A research team at the University of Virginia School of Engineering and Applied Science has developed what it believes could be the template for the first building blocks for human-compatible organs printed on demand.
Liheng Cai, an assistant professor of materials science and engineering and chemical engineering, and his Ph.D. student, Jinchang Zhu, have made biomaterials with controlled mechanical properties matching those of various human tissues.
“That’s a big leap compared to existing bioprinting technologies,” Zhu said.
Brain-inspired navigation technologies combine environmental perception, spatial cognition, and target navigation to create a comprehensive navigation research system. Researchers have used various sensors to gather environmental data and enhance environmental perception using multimodal information fusion. In spatial cognition, a neural network model is used to simulate the navigation mechanism of the animal brain and to construct an environmental cognition map. However, existing models face challenges in achieving high navigation success rate and efficiency. In addition, the limited incorporation of navigation mechanisms borrowed from animal brains necessitates further exploration.
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.
