This architecture is called a pattern associator . LTU's can only produce linear discriminant functions and consequently, they are limited in the kinds of classes that can be learned. This seemed in the 1960s to kill off perceptron-based learning. However, subsequently a generaliz ation of perceptrons was found that surmounted this problem. The place where an axon of one neuron connects to a dendrite of another neuron is termed a synapse .
Synapses differ in the effect that they have on the output of the neuron.
Some are strongly excitatory, some weakly excitatory, some weakly inhibitory, and so on. The incoming signals from the axons of other neurons correspond to the inputs yi to the node. The strength of the synapse corresponds to weight wi associated with a node. In addition to this, each node in an MLP includes a nonlinearity at its output end. The overall layout is illustrated in Figure 5.