Threshold and saturation are two nonlinear features common to almost all spiking neurons. How these nonlinearities affect the performance gain of the transfer function and coding properties of the neurons has attracted much attention. Here, we deduce basic analytical relationships among these nonlinearities (threshold and saturation), performance gain and information transmission in neurons. We found that performance gain and information transmission can be maximized by input signals with optimal variance. The threshold and saturation inside the model determines the gain tuning property and maximum coding capacity. This framework provides an understanding of some basic design principles underlying information processing systems that can be adjusted to match the statistics of signals in the environment. This study also isolates the exact contributions of the nonlinearities on the contrast adaptation phenomena observed in real visual neurons.