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.
A nanocomponent that is capable of learning: The Bielefeld memristor built into a chip here is 600 times thinner than a human hair.
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 prestigious Journal of Physics published by the Institute of Physics in London.
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.
Thanks to these properties, synapses can be used to reconstruct the brain process responsible for learning, says Andy Thomas. He takes the classic psychological experiment with Pavlov’s dog as an example. The experiment shows how you can link the natural reaction to a stimulus that elicits a reflex response with what is initially a neutral stimulus – this is how learning takes place. If the dog sees food, it reacts by salivating. If the dog hears a bell ring every time it sees food, this neutral stimulus will become linked to the stimulus eliciting a reflex response. As a result, the dog will also salivate when it hears only the bell ringing and no food is in sight. The reason for this is that the nerve cells in the brain that transport the stimulus eliciting a reflex response have strong synaptic links with the nerve cells that trigger the reaction.
If the neutral bell-ringing stimulus is introduced at the same time as the food stimulus, the dog will learn. The control mechanism in the brain now assumes that the nerve cells transporting the neutral stimulus (bell ringing) are also responsible for the reaction – the link between the actually ‘neutral’ nerve cell and the ‘salivation’ nerve cell also becomes stronger. This link can be trained by repeatedly bringing together the stimulus eliciting a reflex response and the neutral stimulus. ‘You can also construct such a circuit with memristors – this is a first step towards a neuromorphic processor,’ says Andy Thomas.
‘This is all possible because 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.Original publication:
Five developments for improved data exploitation
19.04.2017 | Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, DFKI
Smart Manual Workstations Deliver More Flexible Production
04.04.2017 | Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, DFKI
The nearby, giant radio galaxy M87 hosts a supermassive black hole (BH) and is well-known for its bright jet dominating the spectrum over ten orders of magnitude in frequency. Due to its proximity, jet prominence, and the large black hole mass, M87 is the best laboratory for investigating the formation, acceleration, and collimation of relativistic jets. A research team led by Silke Britzen from the Max Planck Institute for Radio Astronomy in Bonn, Germany, has found strong indication for turbulent processes connecting the accretion disk and the jet of that galaxy providing insights into the longstanding problem of the origin of astrophysical jets.
Supermassive black holes form some of the most enigmatic phenomena in astrophysics. Their enormous energy output is supposed to be generated by the...
The probability to find a certain number of photons inside a laser pulse usually corresponds to a classical distribution of independent events, the so-called...
Microprocessors based on atomically thin materials hold the promise of the evolution of traditional processors as well as new applications in the field of flexible electronics. Now, a TU Wien research team led by Thomas Müller has made a breakthrough in this field as part of an ongoing research project.
Two-dimensional materials, or 2D materials for short, are extremely versatile, although – or often more precisely because – they are made up of just one or a...
Two researchers at Heidelberg University have developed a model system that enables a better understanding of the processes in a quantum-physical experiment...
Glaciers might seem rather inhospitable environments. However, they are home to a diverse and vibrant microbial community. It’s becoming increasingly clear that they play a bigger role in the carbon cycle than previously thought.
A new study, now published in the journal Nature Geoscience, shows how microbial communities in melting glaciers contribute to the Earth’s carbon cycle, a...
20.04.2017 | Event News
18.04.2017 | Event News
03.04.2017 | Event News
21.04.2017 | Physics and Astronomy
21.04.2017 | Health and Medicine
21.04.2017 | Physics and Astronomy