Reconfigurable Logical Cells Using a Maximum Sensibility Neural Network

In the present article was implemented a maximum sensibility neural network in a reconfigurable logical electronic structure (cell) in which different basic logical functions and combinational logic circuits as comparators, multiplexers and encoders are obtained. This neural network has advantages like easy implementation and a quick learning based on manipulation of the information in place of a gradient algorithm. The reconfiguration of the cell it will realized by modifying one specific input that will change de logical function.