Spectrum-Diverse Neuroevolution with Unified Neural Models (SUNA) has been shown to be a successful alternative to the algorithm NeuroEvolution of Augmenting Topologies (NEAT). Requiring less parameters than NEAT yet possessing a more unified representation power and effective spectrum-based diversity preservation, SUNA outperformed NEAT on most of the problems to be experimented. However, we think a simple improvement approach can be made to improve SUNA's efficiency in the strategic decision-making problem tested by the model itself, i.e. the multiplexer problem. In the proposed method, we try to incorporate the idea of logical gates to the hidden neurons in the model, suggesting it the solutions that solve the problem in the real world in the form of neurons. It is shown that with the simple logic gates neuron variations, SUNA can be slightly enhanced to resolve the multiplexer problem.