  Feedback is crucial to motion perception in animals' visual systems where its
spatial and temporal dynamics are often shaped by movement patterns of
surrounding environments. However, such spatio-temporal feedback has not been
deeply explored in designing neural networks to detect small moving targets
that cover only one or a few pixels in image while presenting extremely limited
visual features. In this paper, we address small target motion detection
problem by developing a visual system with spatio-temporal feedback loop, and
further reveal its important roles in suppressing false positive background
movement while enhancing network responses to small targets. Specifically, the
proposed visual system is composed of two complementary subnetworks. The first
subnetwork is designed to extract spatial and temporal motion patterns of
cluttered backgrounds by neuronal ensemble coding. The second subnetwork is
developed to capture small target motion information and integrate the
spatio-temporal feedback signal from the first subnetwork to inhibit background
false positives. Experimental results demonstrate that the proposed
spatio-temporal feedback visual system is more competitive than existing
methods in discriminating small moving targets from complex dynamic
environment.
