A Biological Grounding of Recruitment Learning and Vicinal Algorithms

Biological neural networks are capable of gradual learning based on observing a large number of exemplars over time as well as rapidly memorizing speciic events as a result of a single exposure. The primary focus of research in connectionist modeling has been on gradual learning, but some researchers have also attempted the computational modeling of rapid (one-shot) learning within a framework described variably as recruitment learning and vicinal algorithms. While general arguments for the neural plausibility of recruitment learning and vicinal algorithms based on notions of neural plasticity have been presented in the past, a speciic neural correlate of such learning has not been proposed. Here it is shown that recruitment learning and vicinal algorithms can be rmly grounded in the biological phenomena of long-term poten-tiation (LTP) and long-term depression (LTD). Toward this end, a computational abstraction of LTP and LTD is presented, and an \algorithm" for the recruitment of binding-detector cells is described and evaluated using biologically realistic data. It is shown that binding-detector cells of distinct bindings exhibit low levels of cross-talk even when the bindings overlap. In the proposed grounding, the speciication of a vicinal algorithm amounts to specifying an appropriate network architecture and suitable parameter values for the induction of LTP and LTD.