connectionism

A section of an artificial neural network. The weight, or strength, of each input is indicated here by the relative size of its connection. The firing threshold for the output neuron, N, is 4 in this example. Hence, N is quiescent unless a combination of input signals is received from W, X, Y, and Z that exceeds a weight of 4.

connectionism, an approach to artificial intelligence (AI) that developed out of attempts to understand how the human brain works at the neural level and, in particular, how people learn and remember. (For that reason, this approach is sometimes referred to as neuronlike computing.) In 1943 the neurophysiologist Warren McCulloch of the University of Illinois and the mathematician Walter Pitts of the University of Chicago published an influential treatise on neural networks and automatons, according to which each neuron in the brain is a simple digital processor and the brain as a whole is a form of computing machine. As McCulloch put it subsequently, “What we thought we were doing (and I think we succeeded fairly well) was treating the brain as a Turing machine.”

B.J. Copeland