Insects such as ants exhibit impressive navigational abilities despite possessing compact nervous systems. This study investigates the computational principles underlying visual memory and route navigation in ants, focusing on the mushroom body (MB), a sparse and plastic neural architecture known to be an important learning center in insects brain. We construct a biologically inspired neural network model and develop a theoretical framework to analyze how the statistical properties of visual inputs affect memory capacity. In the MB, sensory information is encoded by a large population of Kenyon Cells (KCs), each receiving sparse input from projection neurons and activating only when a threshold is crossed.