How to use a memristor to create an artificial brain

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,

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,

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.

A nanocomponent that is capable of learning: The Bielefeld memristor built into a chip here is 600 times thinner than a human hair. [ downloaded from]

A nanocomponent that is capable of learning: The Bielefeld memristor built into a chip here is 600 times thinner than a human hair. [ downloaded from]

Here’s a citation for and link to the paper (from the university news release),

Andy Thomas, ‘Memristor-based neural networks’, Journal of Physics D: Applied Physics,, 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,

As a service to the community, IOP is pleased to make papers in its journals freely available for 30 days from date of online publication – but only fair use of the content is permitted.

Under fair use, IOP content may only be used by individuals for the sole purpose of their own private study or research. Such individuals may access, download, store, search and print hard copies of the text. Copying should be limited to making single printed or electronic copies.

Other use is not considered fair use. In particular, use by persons other than for the purpose of their own private study or research is not fair use. Nor is altering, recompiling, reselling, systematic or programmatic copying, redistributing or republishing. Regular/systematic downloading of content or the downloading of a substantial proportion of the content is not fair use either.

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.

2 thoughts on “How to use a memristor to create an artificial brain

  1. A Sceptic

    Before talking about blueprints, one has to consider that the dynamic state equations describing so-called non-volatile memristors are in conflict with fundamentals of physics. These problems are discussed in:

    “Fundamental Issues and Problems in the Realization of Memristors” by P. Meuffels and R. Soni (

    “On the physical properties of memristive, memcapacitive, and meminductive systems” by M. Di Ventra and Y. V. Pershin (

  2. Pingback: 2.5M Euros for Ireland’s John Boland and his memristive nanowires « FrogHeart

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