Metal nanowire network with brain-like functions. Free Technical Library News

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Metal nanowire network with brain-like features

30.12.2019

An international collaborative research team led by the National Institute of Materials Science (Japan) has succeeded in creating a neuromorphic network composed of numerous metallic nanowires. Using this network, the team was able to create electrical characteristics similar to those associated with higher-order brain functions unique to humans, such as remembering, learning, forgetting, becoming alert, and returning to calm. The team then figured out the mechanisms that triggered these electrical characteristics.

The development of artificial intelligence (AI) techniques in recent years has advanced rapidly and has begun to influence our lives in various ways. Although AI processes information in a similar way to the human brain, the mechanisms by which the human brain works are still largely unknown. The main components of the brain, such as neurons and the connections between them (synapses), have been studied in detail. However, many questions regarding the brain as a whole, consisting of many components, still await answers. For example, we still do not fully understand how the brain performs functions such as remembering, learning, and forgetting, and how it becomes alert and returns to calm. In addition, living brains are difficult to manipulate in experimental studies. For these reasons, the brain remains a "mysterious organ." In the framework of the development of brain sciences, another approach to the study of this organ is effective - in which materials and systems are created that are capable of performing functions similar to those of the brain, and their mechanisms are analyzed.

A collaborative research team recently created a complex brain-like network by integrating numerous silver (Ag) nanowires coated with a polymer (PVP) insulating layer about 1 nanometer thick. The connection between the two nanowires forms a variable resistive element (i.e., a synaptic element) that behaves like a neuronal synapse.

This network of nanowires, which contains a large number of complexly interacting synaptic elements, forms a "neuromorphic network". When voltage was applied to the neuromorphic network, it seemed to be "struggling" to find optimal current paths (i.e., the most electrically efficient paths). The research team measured the processes of forming, holding and deactivating the current path while the electrical current flowed through the network and found that these processes always fluctuate as they progress, similar to the processes of remembering, learning and forgetting the human brain.

The observed temporal fluctuations also resemble processes in which the brain becomes alert or returns to calm. Brain-like functions mimicked by the neuromorphic network are found to occur as the vast number of synaptic elements in the network work together to optimize current transfer through self-organizing and emergent dynamic processes.

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Search for axions by a dark matter hurricane 24.11.2018

A team of scientists from King's College London, the British Astronomical Institute and the University of Zaragoza, Spain, have found that the dark matter 'hurricane' that's passing through our solar system right now provides a great chance to detect axions. We remind our readers that axions are elementary particles that are one of the candidates for the title of dark matter particles, and their detection and study will allow scientists to understand the phenomenon of dark matter and related phenomena.

Modern science already has a number of fairly reliable indirect evidence of the existence of dark matter, despite the lack of the ability to see and "feel" it live. And it is quite natural that scientists continue to look for new ways and technologies that will allow them to touch the "dark secret".

Specialists have focused on the S1 stellar stream, consisting of approximately 30 stars, which moves along a trajectory that indicates that the stars of this stream were once part of a dwarf galaxy swallowed by the Milky Way. The S1 stream was discovered just last year by a team of astronomers studying data collected by the Gaia space telescope. Note that S1 is not the first stellar stream known to scientists, but it is the first and only one whose trajectory of motion intersects with the space of the solar system.

According to the assumptions of scientists, the flow S1 is kept in the form of a single cosmic object by the forces of gravity of the dark matter contained in it. And now this mass of dark matter is moving through the solar system, moving at a speed of about 500 kilometers per second, which, in turn, gives scientists a unique chance to detect and study dark matter particles.

Now scientists have already created a number of mathematical models that demonstrate the distribution of mass and density of moving dark matter. The data obtained during the calculations of these models can serve as a hint for other scientists, according to these data, it is possible to identify areas in which the chance of detecting dark matter increases several times. In addition, the calculation data of the models contains information about what exactly and how it will be necessary to search.

According to the results of calculations, the chance of detecting WIMP particles, which are also candidates for the title of dark matter particles, is extremely small. But the chance of detecting axins in this case is much higher than under normal conditions. This is due to the fact that in the dark matter stream moving together with the stellar stream S1, there may be axions with a wide range of values of their mass and energy.

Unfortunately, the recording scientific equipment available today is not capable of registering axions, despite the wide energy spectrum of their flux. But the S1 stellar stream will cross the space of the solar system for a very long time, and it is likely that until this is over, scientists will have time to develop and use next-generation particle detection systems with the necessary level of their sensitivity.

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