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.
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 summarise 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.
Dr Andy Thomas has summarised 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, 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.