megaman1970

Abstract

Biological materials are self-assembled with near-atomic precision in living cells, whereas synthetic 3D structures generally lack such precision and controllability. Recently, DNA nanotechnology, especially DNA origami technology, has been useful in the bottom-up fabrication of well-defined nanostructures ranging from tens of nanometres to sub-micrometres. In this Primer, we summarize the methodologies of DNA origami technology, including origami design, synthesis, functionalization and characterization. We highlight applications of origami structures in nanofabrication, nanophotonics and nanoelectronics, catalysis, computation, molecular machines, bioimaging, drug delivery and biophysics. We identify challenges for the field, including size limits, stability issues and the scale of production, and discuss their possible solutions. We further provide an outlook on next-generation DNA origami techniques that will allow in vivo synthesis and multiscale manufacturing.

The human brain contains functionally and anatomically distinct networks for representing semantic information in each sensory modality, and a separate, distributed amodal conceptual network. In this study we examined the spatial organization of visual and amodal semantic functional maps. The pattern of semantic selectivity in these two distinct networks corresponds along the boundary of visual cortex: for visual categories represented posterior to the boundary, the same categories are represented linguistically on the anterior side. These results suggest that these two networks are smoothly joined to form one contiguous map.

A unique form of brain stimulation appears to boost people’s ability to remember new information—by mimicking the way our brains create memories.

The “memory prosthesis,” which involves inserting an electrode deep into the brain, also seems to work in people with memory disorders—and is even more effective in people who had poor memory to begin with, according to new research. In the future, more advanced versions of the memory prosthesis could help people with memory loss due to brain injuries or as a result of aging or degenerative diseases like Alzheimer’s, say the researchers behind the work.

“It’s a glimpse into the future of what we might be able to do to restore memory,” says Kim Shapiro, a neuroscientist at the University of Birmingham in the UK, who was not involved in the research.

It works by copying what happens in the hippocampus—a seahorse-shaped region deep in the brain that plays a crucial role in memory. The brain structure not only helps us form short-term memories but also appears to direct memories to other regions for long-term storage.

Summary

Memristive devices share remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level, making the memristive approach to neuromorphic computing a promising technology for future artificial intelligence. However, these similarities do not directly transfer to the success of efficient computation without device and algorithm co-designs and optimizations. Contemporary deep learning algorithms demand the memristive artificial synapses to ideally possess analog weighting and linear weight-update behavior, requiring substantial device-level and circuit-level optimization. Such co-design and optimization have been the main focus of memristive neuromorphic engineering, which often abandons the “non-ideal” behaviors of memristive devices, although many of them resemble what have been observed in biological components. Novel brain-inspired algorithms are being proposed to utilize such behaviors as unique features to further enhance the efficiency and intelligence of neuromorphic computing, which calls for collaborations among electrical engineers, computing scientists, and neuroscientists.