Auto-Associative Memories for Direct Signalling of Visual Angle During Object Approaches

Abstract

Being hit by a ball is usually not a pleasant experience. While a ball may not be fatal, other objects can be. To protect themselves, many organisms, from humans to insects, have developed neuronal mechanisms to signal approaching objects such as predators and obstacles. The study of these neuronal circuits is still ongoing, both experimentally and theoretically. Many computational proposals rely on temporal contrast integration, as it encodes how the visual angle of an approaching object changes with time. However, mechanisms based on contrast integration are severely limited when the observer is also moving, as it is difficult to distinguish the background-induced temporal contrast from that of an approaching object. Here, I present results of a new mechanism for signaling object approaches, based on modern content-addressable (auto-associative) memories. Auto-associative memories were first proposed by Hopfield in 1982, and are a class of simple neuronal networks which transform incomplete or noisy input patterns to complete and noise-free output patterns. The memory holds different sizes of a generic pattern template that is efficient for segregating an approaching object from irrelevant background motion. Therefore, the model's output correlates directly with angular size. Generally, the new mechanism performs on a par with previously published models. The overall performance was systematically evaluated based on the network's responses to artificial and real-world video footage.