Russia’s Admiral Grigorovich frigate sails to Mediterranean SeaMilitary & Defense September 25, 11:36
Russian lawmaker calls German election outcome ‘predictable’Russian Politics & Diplomacy September 25, 10:46
Russian-Chinese naval drills ‘Joint Sea 2017’ completed in VladivostokMilitary & Defense September 25, 10:29
Independence referendum underway in Iraqi KurdistanWorld September 25, 9:47
Russia and US have no plans to curtail space cooperationScience & Space September 25, 9:30
Denis Matsuev: That extraordinary idea workedSociety & Culture September 25, 8:00
Tehran's top diplomat slams 'fake' empathy from Trump for IraniansWorld September 25, 6:06
Merkel wins Bundestag electionsWorld September 25, 5:37
Expert says North Korea won’t test thermonuclear warhead in Pacific OceanWorld September 25, 4:26
MOSCOW, April 15. /TASS/. A team of scientists of the Moscow Institute of Physics and Technology (MIPT) have created prototypes of "electronic synapses" based on ultra-thin films of hafnium oxide (HfO2). These prototypes could potentially be used in fundamentally new computing systems, the MIPT press service said.
The group of researchers from MIPT have made memristors based on thin-film hafnium oxide and measuring just 40x40 nm2. This nanostructures are able to change their state (conductivity) depending on the passing charge, exhibiting properties similar to biological synapses. "Using new technology, the memristors were integrated in matrices: in the future this technology may be used to design computers that function similar to biological neural networks", MIPT said.
A synapse is point of connection between neurons, the main function of which is to transmit a signal from one neuron to another. Each neuron may have thousands of synapses, i.e. connect with a large number of other neurons. This means that information can be processed in parallel, rather than sequentially (as in modern computers). This is the reason why "living" neural networks are so immensely effective both in terms of speed and energy consumption in solving large range of tasks, such as image and voice recognition, etc.
Over time, synapses may change their "weight", i.e. their ability to transmit a signal. This property is believed to be the key to understanding the learning and memory functions of the brain. From the physical point of view, synaptic "memory" and "learning" in the brain can be interpreted as follows: the neural connection possesses a certain "conductivity", which is determined by the previous "history" of signals that have passed through the connection. If a synapse transmits a signal from one neuron to another, we can say that it has high "conductivity", and if it does not, we say it has low "conductivity". However, synapses do not simply function in on/off mode; they can have any intermediate "weight" (intermediate conductivity value). Accordingly, if we want to simulate them using certain devices, these devices will also have to have analogous characteristics.