Professor S Hall and Dr J S Marsland
Neuroscientists now know that the human brain is made up of millions of small units, called neurons, which are connected to each other in a very complex way. These neurons carry out relatively simple calculations using the information that enters the brain from our senses (eyes, touch, etc) and the result of these calculations is passed on to other neurons as small electrical signals. Because each neuron performs simple calculations, it is believed that very complex calculations, such as recognising someone, can be achieved when millions of neurons are connected together to form a network, as is the case in the human brain. Engineers and scientists are interested in how the brain carries out these calculations because the computing power of the brain far exceeds that of any man made machine, such as the desktop computer.
Much of the processing of the brain is learned over time. Therefore, to understand how the brain learns to perform complex calculations, engineers and scientists are continually trying to build models of the brain, called artificial neural networks. Much of this modeling is carried out using computers or electronic circuits that mimic neuron behaviour. The problems facing engineers and scientists in designing electronic neurons are:
1) designing circuits that behave like neurons
2) making circuits small enough so that millions of them can be placed on a silicon chip
3) these neurons must consume minimal power
Since there are no available electronic components that can mimic the components of neurons, what is required is the development of new electronic components with small physical dimensions that operates just like real neurons and consume minimal power. This is what we are trying to achieve.
The research will involve the design, development and testing of the electronic neuron and subsequently a learning algorithm will be developed that can train a neural network made up of these neurons to recognise artifacts of the real world.