Object recognition is one of the most important tasks in computer vision due to its wide variety of applications from small hand-held devices to surveillance systems in large public facilities. Even though biologically inspired approaches have been recently revealed to take another significant step forward to reduce its large power consumption, it still consumes relatively large amounts of energy because of the immense amount of data and computations. Typically in such biologically inspired - often called neuromorphic - object recognition implementations, visual saliency feeds feature extraction to limit the amount of computations effectively by picking a pre-determined size of patches around salient locations of an image. In this work, we explore the design space of HMAX for neuromorphic feature-extraction and classification along with the trade-off between energy consumption and classification accuracy. In addition, a novel method to further reduce energy consumption is proposed by leveraging effort-level of HMAX according to the findings of visual saliency in an efficient manner. Experiments revealed that our dynamic configuration achieved 70.57% of energy reduction with only 1.05% of accuracy loss for accuracy-critical applications. For energy-critical applications, a proposed configurations trades off 5.07% accuracy to gain 91.72% reduction in energy consumption.