Narrowing the gap between biological brains and electronic ones
SINCE nobody really knows how brains work, those researching them must often resort to analogies. A common one is that a brain is a sort of squishy, imprecise, biological version of a digital computer. But analogies work both ways, and computer scientists have a long history of trying to improve their creations by taking ideas from biology. The trendy and rapidly developing branch of artificial intelligence known as “deep learning”, for instance, takes much of its inspiration from the way biological brains are put together.
The general idea of building computers to resemble brains is called neuromorphic computing, a term coined by Carver Mead, a pioneering computer scientist, in the late 1980s. There are many attractions. Brains may be slow and error-prone, but they are also robust, adaptable and frugal. They excel at processing the sort of noisy, uncertain data that are common in the real world but which tend to give conventional electronic computers, with their prescriptive arithmetical approach, indigestion. The latest development in this area came on August 3rd, when a group of researchers led by Evangelos Eleftheriou at IBM’s research laboratory in Zurich announced, in a paper published in Nature Nanotechnology, that they had built a working, artificial version of a neuron.
Neurons are the spindly, highly interconnected cells that do most of the heavy lifting in real brains. The idea of making artificial versions of them is not new. Dr Mead himself has experimented with using specially tuned transistors, the tiny electronic switches that form the basis of computers, to mimic some of their behaviour. These days, though, the sorts of artificial neurons that do everything from serving advertisements on web pages to recognising faces in Facebook posts are mostly simulated in software, with the underlying code running on ordinary silicon. That works, but as any computer scientist will tell you, creating an ersatz version of something in software is inevitably less precise and more computationally costly than simply making use of the thing itself.
Learn more: Artificial neurons – You’ve go a nerve