Dr. Andy Thomas of Bielefeld University’s (Germany) Faculty of Physics has developed a ‘blueprint’ for an artificial brain based on memristors. From the Feb. 26, 2013, news item on phys.org,
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. Privatdozent [senior lecturer] Dr. Andy Thomas from Bielefeld University’s Faculty of Physics is experimenting with memristors – electronic microcomponents that imitate natural nerves. Thomas and his colleagues proved that they could do this a year ago. They constructed a memristor that is capable of learning. Andy Thomas is now using his memristors as key components in a blueprint for an artificial brain. He will be presenting his results at the beginning of March in the print edition of the Journal of Physics D: Applied Physics.
The Feb. 26, 2013 University of Bielefeld news release, which originated the news item, describes why memristors are the foundation for Thomas’s proposed artificial brain,
Here’s a citation for and link to the paper (from the university news release),
Memristors are made of fine nanolayers and can be used to connect electric circuits. 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.
Andy Thomas explains 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.’ Based on his own experiments and research findings from biology and physics, his article is the first to summarize which principles taken from nature need to be transferred to technological systems if such a neuromorphic (nerve like) computer is to function. Such principles are that memristors, just like synapses, have to ‘note’ earlier impulses, and that neurons react to an impulse only when it passes a certain threshold.
‘… a memristor can store information more precisely than the bits on which previous computer processors have been based,’ says Thomas. Both a memristor and a bit work with electrical impulses. However, a bit does not allow any fine adjustment – it can only work with ‘on’ and ‘off’. In contrast, a memristor can raise or lower its resistance continuously. ‘This is how memristors deliver a basis for the gradual learning and forgetting of an artificial brain,’ explains Thomas.
Andy Thomas, ‘Memristor-based neural networks’, Journal of Physics D: Applied Physics, http://dx.doi.org/10.1088/0022-3727/46/9/093001, released online on 5 February 2013, published in print on 6 March 2013.
This paper is available until March 5, 2013 as IOP Science (publisher of Journal Physics D: Applied Physics), makes their papers freely available (with some provisos) for the first 30 days after online publication, from the Access Options page for Memristor-based neural networks,
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Getting back to the memristor, I’ve been writing about it for some years, it was most recently mentioned here in a Feb.7, 2013 posting and I mentioned in a Dec. 24, 2012 posting nanoionic nanodevices also described as resembling synapses.