Now suppose that for a given input the final output ought to be +1 but actually is -1. Assume that b is then raised so high that this final output is corrected. Then commence a gradual decline in b. Various elements may revert to -1, but until the final output does, no weights are changed. When the final output does revert to -1, it is due to an element’s having a sum (weighted sum plus bias) which just passed down through zero. This then caused a chain effect of changing elements up to the final element, but presumably this element is the only one possessing a zero sum. This can then be the signal for the weights on an element to change—a change of final output from right to wrong accompanied simultaneously by a zero sum in the element itself. After such a weight change, the final output will be correct once more and the bias can again proceed to fall. Before it reaches zero, this process may occur a number of times throughout the network. When the bias finally stands at zero with the final output correct, the network is ready for the next input. Of course if -1 is desired, the bias will change in the opposite direction. It is possible that extending the weight change process a little past the zero bias level may have beneficial results. This might increase the life expectancy of each learned input-output combination and thereby reduce the total number of errors. This is because the method used above can stop the weight correction process so that even though the final output is correct, some elements whose output are essential to the final output have sums close to zero, which are easily changed by subsequent weight changes. It will be noted that this method conforms to all three considerations mentioned previously. First, by furnishing each element the same bias, and by not changing weights until the final output becomes incorrect with dropping bias, there is a strong tendency to select elements which, with b = 0, would have sums close to zero. But the size of the sum in an element is a good measure of the amount of damage done to an element for other inputs if its current output is to be changed. Second, it is obvious that each element changed has had a demonstrable effect on the final output. Finally, there will be a clear tendency to change only a minimum of elements because changes never occur until the output clearly requires a change.