Research into new drugs for brain disease is being held back because of a lack of fundamental understanding and models of brain function argues Steve Furber, who explains why policy makers and researchers worldwide should make computer modelling part of the solution.
Brain diseases cost the developed economies more than heart diseases, cancer and diabetes put together, not to mention their impact on the quality of life of those affected and their families. Yet research into new drugs for brain diseases has all but stopped, because modern drug development is based on understanding disease processes and that same level of understanding is missing for the brain.
I believe that medical scientists should embrace research undertaken by their colleagues in computer science because it is widely accepted that we now have the tools, not only computers for modelling, but also brain imaging machines, multi-electrode probes, and many more, that make this the right time to try to push forward our understanding of this most complex of organs.
So why should we bother trying to build computer models of the brain at all?
There are three answers to this question.
Firstly, this is a very effective way to advance the science, and the quest to understand our own brains and minds remains as one of the great frontiers of science. As the late, great scientist Richard Feynman once said: “What I cannot create, I do not understand.”
Secondly, a computer model of the brain would be very useful for understanding diseases of the brain, which is vital for developing new treatments.
Thirdly, understanding the brain is likely to lead to insights that can be used to build better and more efficient computer systems.
These three threads: future neuroscience, future medicine and future computing underpin the one billion euro flagship Human Brain Project – a pan-European project that is delivering a range of ICT platforms to support brain research.
These are exciting times for brain research, with major projects not only in Europe but also in the USA, China, Australia and elsewhere. Here in Manchester, we too are playing our role in the shape of the SpiNNaker (short for Spiking Neural Network architecture) – a computing platform optimized to support real-time brain models. SpiNNaker can be used to model areas of the brain and to test new hypotheses about how the brain might work. Because it runs at the same speed as the biological brain it can be interfaced to robotic systems. This biological approach is quite different from the very mathematical algorithmic control systems more commonly used in robotics.
Computer modeling of the brain may also help progress in artificial intelligence. Although research into artificial intelligence has already delivered in many areas of life – think of Google, talking to your smartphone, driverless cars, and so on – it has failed to deliver as expected, particularly by many imaginative science fiction writers, in the area known as Artificial General Intelligence. This is the idea that a suitably programmed machine might display aspects of intelligence that we normally associate only with humans. My take on the failure to deliver this form of artificial intelligence is that we have never actually worked out what natural intelligence is, so we don’t know what it is that we are trying to imitate in our machines.
As a result, in my research I have gone back to the source of human intelligence – the human brain – and tried to see how we might use computers to better understand this mysterious organ upon which we all so critically depend.
We do not yet understand the nature of human intelligence because it has many dimensions: it is not simply the ability to understand maths, science, or the Arts. Think, for example, of the intelligence required to kick a leather sphere into the back of a net past other humans who are doing their best to stop you. This is clearly a form of intelligence. Indeed, it would appear to be the form of intelligence most valued by many in our current human society!
In his seminal 1950 paper Computing Machinery and Intelligence Alan Turing began by considering the question: “Can machines think?” He then went on to suggest that this question is difficult to answer directly, and he turned it around into a research experiment that he called ‘The Imitation Game’, but which subsequent generations simply know as the Turing test for Artificial Intelligence. The test – in a nutshell – aims to find out if a computer can fool an interrogator into thinking that it is human.
It is worth remembering that Turing wrote this paper only two years after the world’s first operational electronic stored-program computer ran its first program at The University of Manchester, on June 21, 1948. Indeed, it was this machine that brought Turing to The University of Manchester and it was during his time here that he wrote this paper. Turing thought that all that a machine would require to pass his test was more memory – the 1948 Manchester ‘Baby’ computer was quite powerful enough already. He estimated that a gigabyte (a thousand million bytes) of memory should suffice, and this should be achievable by the end of the twentieth century.
By the beginning of the 21st century computers did, indeed, typically have a gigabyte of memory, and they were a million times faster than the ‘Baby’, but still they could not pass his test.
Even today, with still far more computing power and memory, no machine has convincingly passed the test. This would have surprised Turing had he lived to see it!