Connectionism traces back to McCulloch and Pitts (1943), who studied networks of interconnected logic gates (e.g., AND-gates and OR-gates). One can view a network of logic gates as a neural network, with activations confined to two values (0 and 1) and activation functions given by the usual truth-functions. McCulloch and Pitts advanced logic gates as idealized models of individual neurons. Their discussion exerted a profound influence on computer science (von Neumann 1945). Modern digital computers are simply networks of logic gates. Within cognitive science, however, researchers usually focus upon networks whose elements are more “neuron-like” than logic gates. In particular, modern-day connectionists typically emphasize analog neural networks whose nodes take continuous rather than discrete activation values. Some authors even use the phrase “neural network” so that it exclusively denotes such networks.