Stability of Logical Networks and its Application to Improve-ment of Reliability

Abstract

McCulloch showed that certain redundancy networks composed of neuron-like elements exhibit the property of even though the individual neurons may undergo transformation of states caused by shift in neural threshold. It was speculated that the stability of neural nets contributes to the improved behaviorial reliability of human beings, a problem that was originally raised by Von Neumann. This paper extends the concept of fallible neurons to conventional logic modules as found in technological systems. It is shown that in a single logic module, transformation of states may occur due to malfunction of the internal components and/or drift in the thresholds of the signal and control inputs. The existence of multiple states in a single module lends itself to the possibility of constructing networks with redundant modules which are functionally more stable than the individual modules. Finally, it is shown that suitably chosen, logically stable structures may be used to replace the individual modules in order to perform logical operations with greater reliability.