  Human vision incorporates non-uniform resolution retina, efficient eye
movement strategy, and spiking neural network (SNN) to balance the requirements
in visual field size, visual resolution, energy cost, and inference latency.
These properties have inspired interest in developing human-like computer
vision. However, existing models haven't fully incorporated the three features
of human vision, and their learned eye movement strategies haven't been
compared with human's strategy, making the models' behavior difficult to
interpret. Here, we carry out experiments to examine human visual search
behaviors and establish the first SNN-based visual search model. The model
combines an artificial retina with spiking feature extraction, memory, and
saccade decision modules, and it employs population coding for fast and
efficient saccade decisions. The model can learn either a human-like or a
near-optimal fixation strategy, outperform humans in search speed and accuracy,
and achieve high energy efficiency through short saccade decision latency and
sparse activation. It also suggests that the human search strategy is
suboptimal in terms of search speed. Our work connects modeling of vision in
neuroscience and machine learning and sheds light on developing more
energy-efficient computer vision algorithms.
