Biological Grounding of Recruitment Learning and Vicinal Algorithms in Long-Term Potentiation

Abstract

Biological networks are capable of gradual learning based on observing a large number of exemplars over time as well as of rapidly memorizing specific events as a result of a single exposure. The focus of research in neural networks has been on gradual learning, and the modeling of one-shot memorization has received relatively little atten- tion. Nevertheless, the development of biologically plausible computa- tional models of rapid memorization is of considerable value, since such models would enhance our understanding of the neural processes un- derlying episodic memory formation. A few researchers have attempted the computational modeling of rapid (one-shot) learning within a frame- work described variably as recruitment learning and vicinal algorithms. Here it is shown that recruitment learning and vicinal algorithms can be grounded in the biological phenomena of long-term potentiation and long-term depression. Toward this end, a computational abstraction of LTP and LTD is presented, and an “algorithm” for the recruitment of binding-detector (or coincidence-detector) cells is described and evaluated using biologically realistic data.