Memristor is an electrical component that limits or regulates the flow of electrical current in a circuit and remembers the amount of charge that has previously flowed through it. Memristors are important because they are non-volatile, meaning that they retain memory without power.
A collaborative of Russian and Italian scientists has created a neural network based on polymeric memristors, devices that can potentially be used to build fundamentally new computers. According to the researchers, these developments have applications in systems for machine vision, hearing, and other sensory organs, and also intelligent control systems for various devices, including autonomous robots.
The experiments reported demonstrate that it is possible to create very simple polyaniline-based neural networks. Furthermore, these networks are able to learn and perform specified logical operations.
A memristor is an electric element similar to a conventional resistor. The difference between a memristor and a traditional element is that the electric resistance in a memristor is dependent on the charge passing through it. Therefore, it constantly changes its properties under the influence of an external signal—a memristor has a memory and at the same time is also able to change data encoded by its resistance state. In this sense, a memristor is similar to a synapse, a connection between two neurons in the brain with a high level of plasticity that is able to modify the efficiency of signal transmission between neurons under the influence of the transmission itself. A memristor enables scientists to build a true neural network, and the physical properties of memristors mean that at a minimum, they can be made as small as conventional chips.
Devices that are fundamentally the same as neural networks could be used for a variety of tasks. Most importantly, neural networks are capable of pattern recognition; they are used as a basis for recognising handwritten text for example, or signature verification. When a certain pattern needs to be recognised and classified, such as a sound, an image, or characteristic changes on a graph, neural networks are actively used and it is in these fields where gaining an advantage in terms of speed and energy consumption is critical. In a control system for an autonomous flying robot every milliwatt-hour and every millisecond counts, just in the same way that a real-time system to process data from a collider detector cannot take too long to “think” about highlighting particle tracks that may be of interest to scientists from among a large number of other recorded events.
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