The use of neurons with higher functionality to enhance the fault tolerance of neural networks

So far we have proposed fault-tolerant design techniques of neural networks based on the property of conventional neurons (component elements of neural networks). If a higher functionality of each neuron is available rather than simple weighted sum of the inputs, we can design neural networks which tolerate the mixture of stuck-at-1 and stuck-at-0 faults without exploiting the triplication scheme