Retrospective learning of spatial invariants during object classification by embodied autonomous neural agents

This paper presents a new semi-supervised neural architecture that learns to classify objects at a distance through experience. It utilizes Fuzzy LAPART extended with two temporal integrator subnetworks to create time-stamped perceptual memory codes in an unsupervised manner during object approach, and to retrospectively learn class code inferences at contact. Fuzzy LAPART, Temporal Integrators and the integrated architecture are presented. Next, the agent-based modeling and neural simulator tools used to model this architecture are described. Finally, a study is presented that illustrates the learning performance of this architecture embodied in a simple simulated agent moving in a 2D environment.