Building Computers to Work like the Brain

Neural networks are not cognitive machines. But they achieve in a way more like biological brains than digital computers. This shows in their ability to handle imprecise data and to learn from experience.

The capabilities of a neural computer depend entirely on the number of processors and the complexity of connections between them. In this respect it differs from a conventional, sequential computer. A desktop PC can do everything a mainframe can do; the smaller machine would just take longer to do it. The human brain contains something like 10 billion neurons. So far, researchers have managed to build neural networks that consist of only a few dozen processors: building more complex networks poses formidable practical and theoretical problems.

The neurons in such a computer network are simple processors or switching devices that receive data from several inputs. When the amount of data reaches a threshold value, the neurons fore a more powerful output signal.

A classical neural network has three layers of processors: an input layer, an output layer and a hidden layer in between. A network of synapses connects each processor to all the others in the network: each neuron in the input layer is connected to each neuron in the hidden layer, which is in turn connected to each neuron in the output layer.

The data to be processed go first to the input layer. In a pattern-recognition machine, for instance, this data might be the image of the handwritten letter “A”. Each input neuron that receives enough data to trigger an output signal, relays that data to the hidden layer, which, in the same way, relays the data to the output layer.

The overall effect is of a device that can sum up a lot of impressive information and decide whether it matches a pattern or not. If the formation does match the image of the letter “A”, for instance, the output layer makes the computer respond to the signal. A conventional computer, no matter how powerful, must make the decision from the first input of the imprecise data-if that decision is wrong, so are all later operations.

The principle of neural networks is the same regardless of whether they work electronically or optically, or exist merely in the software of a conventional computer. Biological brains, however, are much more subtle than these crude artificial networks. Each set of biological signals, which is carried by a cascade of chemical reactions between neurons, seems to modify the network as it passes through. Researchers need to do a lot more work on brains before they will understand them, let alone build artificial replicas.