Memristors are used as the key components in a blueprint for an artificial brain. Memristors are electronic microcomponents that imitate natural nerves. Scientists have long been dreaming about building a computer that would work like a brain. This is because a brain is far more energy-saving than a computer, it can learn by itself, and it doesn't need any programming.
Dr. Andy Thomas from Bielefeld University's Faculty of Physics and his colleagues constructed a memristor that is capable of learning a year ago.
He is now is experimenting with his memristors for building an artificial brain.
For several years now, the memristor has been considered to be the electronic equivalent of the synapse. Synapses are, so to speak, the bridges across which nerve cells (neurons) contact each other. Their connections increase in strength the more often they are used. Usually, one nerve cell is connected to other nerve cells across thousands of synapses.
Like synapses, memristors learn from earlier impulses. In their case, these are electrical impulses that (as yet) do not come from nerve cells but from the electric circuits to which they are connected. The amount of current a memristor allows to pass depends on how strong the current was that flowed through it in the past and how long it was exposed to it.
Thomas said that because of their similarity to synapses, memristors are particularly suitable for building an artificial brain - a new generation of computers.
"They allow us to construct extremely energy-efficient and robust processors that are able to learn by themselves," he stated.
Dr. Thomas has summarized the technological principles that need to be met when constructing a processor based on the brain.
Thanks to these properties, synapses can be used to reconstruct the brain process responsible for learning, said Thomas.
He will be presenting his results at the beginning of March in the print edition of the prestigious Journal of Physics published by the Institute of Physics in London.