Information processing in animals' brain requires the balance between excitatory and inhibitory neural circuits. Feedback inhibition is involved in many sensory processes; however, the role of inhibition in system efficiency is not fully understood. Moreover, the regulation of inhibition intensity in response to different stimulus intensity is not fully studied in normal and pathological cases. In this work, a geometrical measure for system efficiency is defined that measures the system ability to discriminate between similar stimulus intensities. For this purpose, we developed a simulation of a two-layer feedforward neural system constrained by electrophysiological data. The effect of inhibition on system efficiency was studied for different feedback inhibition parameter values. The simulations show that inhibition is critically required to detect fluctuations in stimulus intensity, especially for high stimulus intensities. Moreover, simulations demonstrate that incremental change of inhibition parameter value (by a hypothetical homeostatic regulation mechanism) to detect fluctuations in incremental stimulus intensity is critically required to obtain high level of system efficiency. This work assigns a vital role for feedback inhibition in system efficiency of feedforward neural systems.