We start with some arbitrary set of weights, present the letter A, and calculate some error term based on the difference between the desired output and the actual output. After some number of cycles with the whole character set and many slight but hopefully recognizable variations or corruptions of the characters, the net has learned to recognize the 32 characters. Example - In this case, we have the same large set of many characters corresponding to slight variations of the 32 characters, but we want the network to find out on its own that they fall into 32 different categories, and to be able to take an input and assign it to the proper category. Not only is this more natural, but for some problems, we don't know the categories. The neuron receives inputs from excitatory synapses, all having identical weights.
Inhibitory inputs have an absolute veto power over any excitatory inputs.
Using this scheme we can figure out how to implement any Boolean logic function. AND, you can build up XOR's, adders, shift registers, and anything you need to perform computation. Explain to your own satisfaction that this generates the correct output for the four combinations of inputs. These results were very encouraging, but these networks displayed no learning. One had to figure out the weights and connect up the neurons in the appropriate manner to perform the desired computation.