Karlheinz Meier in an IEEE Spectrum special issue on Can We Copy the Brain?:
The idea of using the brain as a model of computation has deep roots. The first efforts focused on a simple threshold neuron, which gives one value if the sum of weighted inputs is above a threshold and another if it is below. The biological realism of this scheme, which Warren McCulloch and Walter Pitts conceived in the 1940s, is very limited. Nonetheless, it was the first step toward adopting the concept of a firing neuron as an element for computation.
In 1957, Frank Rosenblatt proposed a variation of the threshold neuron called the perceptron. A network of integrating nodes (artificial neurons) is arranged in layers. The “visible” layers at the edges of the network interact with the outside world as inputs or outputs, and the “hidden” layers, which perform the bulk of the computation, sit in between.
Rosenblatt also introduced an essential feature found in the brain: inhibition. Instead of simply adding inputs together, the neurons in a perceptron network could also make negative contributions. This feature allows a neural network using only a single hidden layer to solve the XOR problem in logic, in which the output is true only if exactly one of the two binary inputs is true. This simple example shows that adding biological realism can add new computational capabilities. But which features of the brain are essential to what it can do, and which are just useless vestiges of evolution? Nobody knows.
We do know that some impressive computational feats can be accomplished without resorting to much biological realism. Deep-learning researchers have, for example, made great strides in using computers to analyze large volumes of data and pick out features in complicated images. Although the neural networks they build have more inputs and hidden layers than ever before, they are still based on the very simple neuron models. Their great capabilities reflect not their biological realism, but the scale of the networks they contain and the very powerful computers that are used to train them. But deep-learning networks are still a long way from the computational performance, energy efficiency, and learning capabilities of biological brains.