A discretely adaptive connection logic network

Abstract

This paper proposes a new model for a theoretical neural network that can be used as a guide for the design of future (quantum or optical) computational technologies. The model utilizes a uniformly connected nodal structure, where the connections are discretely adaptive and the nodes act as simple gatekeepers. The model replicates all known logics used in current electronics, such as AND, OR, XNOR, XOR, NOR, XNOR, NAND and NOT. Additionally, by using recurrent negating connections the model easily creates XOR gates, and adds novel sided and favoured gates. This model also facilitates the creation of ternary to n-ary gates, and it simplifies the creation of a number of majority functions (especially for an odd number of inputs). Also, as a multi-layered neural network, the model allows learning back propagation through the use of its negating connections. Finally, because of its adaptive connections, parts of the network can be used as internal memory. Overall, the model provides backward compatibility to existing CMOS circuitry, while opening up a number of new logics and architectures for neural computing.