When large-field IR sensors are quite distant from the scene, targets appear as points, so detection is based on their motion, rather than their structure. The appearance of background and clutter bright points, although eventually rejected as nontargets, unnecessarily burdens tracking algorithms. Typical approaches involve eliminating background prior to tracking, or only looking within dynamic search boxes based on previous-frame target observations. This presentation describes a method for distributed predetection of points due to moving targets, in which background points are be automatically rejected, and only those detector returns that are most likely to be from targets are be provided to the tracking algorithm. The paper discusses the retinally inspired concepts behind the proposed method, analytical and empirical evaluations of its performance, and a hardware implementation based on Mead's (1989) analog VLSI circuits, resulting in a fine-grained-parallel architecture suitable for on-focal-plane applications.